Advances in Mobile Robotics Navigation

At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks. Full article

This paper proposes a novel approach for appearance-based loop closure detection using incremental Bag of Words (BoW) with gradient orientation histograms. The presented approach involves dividing and clustering image blocks into local region features and representing them using gradient orientation histograms. To improve the efficiency of the loop closure detection process, the vocabulary Clustering Feature (CF) tree is generated and updated in real time using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm, which is combined with an inverted index for the efficient selection of candidates and calculation of similarity. Moreover, temporally close and highly similar images are grouped to generate islands, which enhances the accuracy and efficiency of the loop closure detection process. The proposed approach is evaluated on publicly available datasets, and the results demonstrate high recall and precision. Full article

Robotic agents are now ubiquitous in both home and work environments; moreover, the degree of task complexity they can undertake is also increasing exponentially. Now that advanced robotic agents are commonplace, the question for utilisation becomes how to enable collaboration of these agents, and indeed, many have considered this over the last decade. If we can leverage the heterogeneous capabilities of multiple agents, not only can we achieve more complex tasks, but we can better position the agents in more chaotic environments and compensate for lacking systems in less sophisticated agents. Environments such as search and rescue, agriculture, autonomous vehicles, and robotic maintenance are just a few examples of complex domains that can leverage collaborative robotics. If the use of a robotic agent is fruitful, the question should be: How can we provide a world state and environment mapping, combined with a communication method, that will allow these robotic agents to freely communicate? Moreover, how can this be decentralised such that agents can be introduced to new and existing environments already understood by other agents? The key problem that is faced is the communication method; however, when looking deeper we also need to consider how the change of an environment is mapped while considering that there are multiple differing sensors. To this end, we present the voxel grid approach for use in a decentralised robotic colony. To validate this, results are presented to show how the single-agent and multiagent systems compare. Full article

There are numerous prospects for automated unmanned forklifts in the fields of intelligent logistics and intelligent factories. However, existing unmanned forklifts often operate according to offline path planning first followed by path tracking to move materials. This process does not meet the needs of flexible production in intelligent logistics. To solve this problem, we proposed an optimized online motion planner based on the output of the state grid as the original path. Constraints such as vehicle kinematics; dynamics; turning restriction at the end of the path; spatial safety envelope; and the position and orientation at the starting point and the ending point were considered during path optimization, generating a precise and smooth trajectory for industrial forklifts that satisfied non-holonomic vehicle constraints. In addition, a new rapid algorithm for calculating the spatial safety envelope was proposed in this article, which can be used for collision avoidance and as a turning-angle constraint term for path smoothing. Finally, a simulation experiment and real-world tray-insertion task experiment were carried out. The experiments showed that the proposal was effective and accurate via online motion planning and the tracking of automated unmanned forklifts in a complicated environment and that the proposal fully satisfied the needs of industrial navigation accuracy. Full article

Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario. Full article

The key module for autonomous mobile robots is path planning and obstacle avoidance. Global path planning based on known maps has been effectively achieved. Local path planning in unknown dynamic environments is still very challenging due to the lack of detailed environmental information and unpredictability. This paper proposes an end-to-end local path planner n-step dueling double DQN with reward-based $ϵ$-greedy (RND3QN) based on a deep reinforcement learning framework, which acquires environmental data from LiDAR as input and uses a neural network to fit Q-values to output the corresponding discrete actions. The bias is reduced using n-step bootstrapping based on deep Q-network (DQN). The $ϵ$-greedy exploration-exploitation strategy is improved with the reward value as a measure of exploration, and an auxiliary reward function is introduced to increase the reward distribution of the sparse reward environment. Simulation experiments are conducted on the gazebo to test the algorithm’s effectiveness. The experimental data demonstrate that the average total reward value of RND3QN is higher than that of algorithms such as dueling double DQN (D3QN), and the success rates are increased by 174%, 65%, and 61% over D3QN on three stages, respectively. We experimented on the turtlebot3 waffle pi robot, and the strategies learned from the simulation can be effectively transferred to the real robot. Full article

This article is focused on the problematics of path planning, which means finding the optimal path between two points in a known environment with obstacles. The proposed path-planning method uses the wavefront algorithm, and two modifications are implemented and verified. The first modification is the removal of redundant waypoints. The first modification is applied because the wavefront algorithm generates redundant waypoints. These waypoints cause unnecessary changes in the direction of movement. The second one is smoothing the generated trajectory using B-spline curves. The reason for applying the second modification is that trajectory generated by the wavefront algorithm is in the form of the polyline, which is inadequate in terms of the smoothness of the robot’s motion. The verification of the proposed method is performed in environments with different densities of obstacles compared with standard Dijkstra’s and A* algorithms. Full article

In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles. Full article

The mobile manipulator is a floating base structure with wide space operability. An integrated mechanical device for mobile operation is formed through the organic combination of the mobile platform and multi-axis manipulator. This paper presents a general kinematic modeling method for mobile manipulators and gives the relevant derivation of the dynamic model. Secondly, the null-space composition of the mobile manipulator is analyzed, the task space is divided, and a variety of task-switching criteria are designed. Finally, a hybrid control model combining dynamic feedback and synovial control based on dynamic parameter identification is designed, and stability proof is given. The theoretical method is also verified by the experimental platform. The proposed method can effectively improve the control accuracy of the mobile manipulator, and the hybrid control method can effectively control the output torque to reach the ideal state. Full article

Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward–backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios. Full article

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments. Full article

Autonomous navigation technology is the basis of underground crawler walking equipment automation. Based on research of the sector laser pose parameter detection method, a pose detection system based on a cross laser is proposed. The mathematical model between pose parameters, laser receiver measurement data and tilt sensor measurement data is established by vector algorithm and the expression of key pose parameters is deduced. This paper studies the deviation correction control strategy of crawler walking equipment and puts forward the point deviation correction control strategy based on roadway excavation technology. Firstly, the end position of the path is determined according to the initial pose of the roadheader. Then, Bessel curve is used to plan the path between the starting point and the endpoint, and the pure tracking algorithm is used to track the planned path. The measurement errors of X and Y coordinates and yaw angle of roadheader within 10 80 m shall not exceed 10 mm, 32 mm and 0.65°. Using the point deviation correction strategy, the positioning error of the roadheader at the end of the path is less than 13 mm, and the yaw angle error is less than 0.16°. In the deviation correction process, the maximum angular velocity of the roadheader is 0.07 rad/s, which is less than 0.82 rad/s of the conventional deviation correction strategy. The results show that the point deviation correction strategy can not only ensure the accuracy of motion control, but also improve the stability of equipment motion. Full article

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems. Full article

Unmanned ground vehicles (UGVs) are making more and more progress in many application scenarios in recent years, such as exploring unknown wild terrain, working in precision agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust and accurate state estimations by using sensor fusion odometry without prior perception and optimization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion model, we propose a robust lidar-inertial odometry with a novel ground condition perception and optimization algorithm specifically designed for UGVs. The probability distribution gained from the raw inertial measurement unit (IMU) measurements during a certain time period and the state estimation of ESKF were both utilized to evaluate the flatness of ground conditions in real-time; then, by analyzing the relationship between the current ground condition and the accuracy of the state estimation, the tightly coupled lidar-inertial odometry was dynamically optimized further by adjusting the related parameters of the processing algorithm of the lidar points to obtain robust and accurate ego-motion state estimations of UGVs. The method was validated in various types of environments with changeable ground conditions, and the robustness and accuracy are shown through the consistent accurate state estimation in different ground conditions compared with the state-of-art lidar-inertial odometry systems. Full article

It is too difficult to directly obtain the correspondence features between the two-dimensional (2D) laser-range-finder (LRF) scan point and camera depth point cloud, which leads to a cumbersome calibration process and low calibration accuracy. To address the problem, we propose a calibration method to construct point-line constraint relations between 2D LRF and depth camera observational features by using a specific calibration board. Through the observation of two different poses, we construct the hyperstatic equations group based on point-line constraints and solve the coordinate transformation parameters of 2D LRF and depth camera by the least square (LSQ) method. According to the calibration error and threshold, the number of observation and the observation pose are adjusted adaptively. After experimental verification and comparison with existing methods, the method proposed in this paper easily and efficiently solves the problem of the joint calibration of the 2D LRF and depth camera, and well meets the application requirements of multi-sensor fusion for mobile robots. Full article

This paper proposes a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on approximately flat ground using a Lidar–IMU–Odometer system. When TWIP is in motion, it is constrained by the ground and suffers from motion disturbances caused by rough terrain or motion shaking. Combining the motion characteristics of TWIP, this paper proposes a framework for localization consisting of a Lidar-IMU-Odometer system. This system formulates a factor graph with five types of factors, thereby coupling relative and absolute measurements from different sensors (including ground constraints) into the system. Moreover, we analyze the constraint dimension of each factor according to the motion characteristics of TWIP and propose a new nonholonomic constraint factor for the odometry pre-integration constraint and ground constraint factor in order to add them naturally to the factor graph with the robot state node on SE(3). Meanwhile, we calculate the uncertainty of each constraint. Utilizing such a nonholonomic constraint factor, a complete lidar–IMU–odometry-based motion estimation system for TWIP is developed via smoothing and mapping. Indoor and outdoor experiments show that our method has better accuracy for two-wheeled inverted pendulum robots. Full article

In this paper, we introduce a self-trained controller for autonomous navigation in static and dynamic (with moving walls and nets) challenging environments (including trees, nets, windows, and pipe) using deep reinforcement learning, simultaneously trained using multiple rewards. We train our RL algorithm in a multi-objective way. Our algorithm learns to generate continuous action for controlling the UAV. Our algorithm aims to generate waypoints for the UAV in such a way as to reach a goal area (shown by an RGB image) while avoiding static and dynamic obstacles. In this text, we use the RGB-D image as the input for the algorithm, and it learns to control the UAV in 3-DoF (x, y, and z). We train our robot in environments simulated by Gazebo sim. For communication between our algorithm and the simulated environments, we use the robot operating system. Finally, we visualize the trajectories generated by our trained algorithms using several methods and illustrate our results that clearly show our algorithm’s capability in learning to maximize the defined multi-objective reward. Full article

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm. Full article

Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency. Full article

In this paper, a longitudinal and lateral control system of an autonomous vehicle is presented by developing a novel hybrid trajectory tracking algorithm. In this proposed method, the longitudinal control system is developed based on the curvature information of the reference path. The autonomous vehicle modifies the desired speed according to the estimated size and types of the reference trajectory curves. This desired speed is integrated into the PID controller to maintain an optimal speed of the vehicle while following the given path. The lateral control system is designed based on feedforward (preview control) and feedback (LQR) controllers to reduce lateral errors between the trajectory and autonomous vehicle. The feedforward and the feedback controllers generate precise steering angles to eliminate orientation and lateral errors caused by the curvature of the trajectory and external disturbances. The effectiveness of the proposed method is evaluated by comparing simulation and experimental results with different trajectory tracking algorithms on simulated and experimented paths. It is proven that the proposed algorithm is capable of significantly minimizing lateral errors on sharp curves compared to other path tracking methods. Full article

This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so that the convergence of this algorithm can be accelerated. The proposed improved GWO algorithm results are compared with the other eight algorithms through several benchmark function test experiments and path planning experiments. The experimental results show that the improved GWO has higher accuracy and faster convergence speed. Full article

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route. Full article

Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the depth code) derived from deep neural networks has been employed in the visual-only or visual-inertial simultaneous localization and mapping (SLAM) systems, which achieve promising performances on both camera motion and local dense geometry estimations from monocular images. However, the existing visual-inertial SLAM systems combined with depth codes are either built on a filter-based SLAM framework, which can only update poses and maps in a relatively small local time window, or based on a loosely-coupled framework, while the prior geometric constraints from the depth estimation network have not been employed for boosting the state estimation. To well address these drawbacks, we propose DiT-SLAM, a novel real-time Dense visual-inertial SLAM with implicit depth representation and Tightly-coupled graph optimization. Most importantly, the poses, sparse maps, and low-dimensional depth codes are optimized with the tightly-coupled graph by considering the visual, inertial, and depth residuals simultaneously. Meanwhile, we propose a light-weight monocular depth estimation and completion network, which is combined with attention mechanisms and the conditional variational auto-encoder (CVAE) to predict the uncertainty-aware dense depth maps from more low-dimensional codes. Furthermore, a robust point sampling strategy introducing the spatial distribution of 2D feature points is also proposed to provide geometric constraints in the tightly-coupled optimization, especially for textureless or featureless cases in indoor environments. We evaluate our system on open benchmarks. The proposed methods achieve better performances on both the dense depth estimation and the trajectory estimation compared to the baseline and other systems. Full article

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state’s estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states. Full article

This article presents a design method of Longitudinal-Bending Coupled Horn (L-BCH) of a giant magnetostrictive transducer utilized in spinning ultrasonic machining. The structural parameters are initially determined by the design theory of the horn and thick disc. Then, the effect of the structural parameters of the rotating wheel on the vibration characteristics of the L-BCH are explored by the model and harmonic response analysis through the finite element method. Through continuous modification of the geometrical parameters of the rotary wheel, the L-BCH meeting the requirements of a giant magnetostrictive transducer is designed. Finally, the frequency and amplitude measurements are performed on the prototype by the impedance analyzer and the laser vibrometer. The finite element analysis and experimental results show that: the large diameter, small diameter, thickness, and fillet radius of the rotating wheel have different impacts on the dynamic characteristics of the L-BCH. Among them, the thickness of the rotary wheel has the most significant influence on the natural frequency and amplitude. In addition, the rotating wheel has a pitch circle when the longitudinal-bending coupled vibration occurs, and the structure itself also has the characteristic of amplifying amplitude. Full article

Realizing robust six degrees of freedom (6DOF) state estimation and high-performance simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as underground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial Odometry (LIO). The state propagation process for the a priori position and attitude of a robot, which uses predictions and observations, increases the accuracy of the attitude and enhances the system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF state estimation. Finally, the performance of the algorithm is verified using a public dataset and the dataset we collected. The experimental results show that for perceptually degraded scenes, compared with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy, real-time performance and robustness, effectively reducing the cumulative error of the system and ensuring the global consistency of the constructed maps. Full article