A Low-Cost 3D Rigid Object Posture Estimation Using 2D LiDAR Dynamics Elevation Mapping System for Autonomous Vehicle

Real-Time lidar-IMU Odometry for Wheeled Robots via 3D-to-2D Feature Projection

As a cutting-edge technology in the AGV industry, 3D lidar SLAM is widely applied in the navigation of wheeled robots due to its ability to provide precise and robust positioning for robots. Compared with 2D lidar SLAM, 3D SLAM often requires more computing resources. This paper proposes a novel method for extracting 3D features from point clouds based on non-repetitive scanning lidar. The 3D feature point clouds are projected into two dimensions, and the improved RANSAC algorithm is applied to a 2D point cloud, realizing robust 2D point cloud registration of unstable feature points. The algorithm was tested on forklift AGV platform. The results show that this method can extract feature points in both indoor and outdoor environments, providing odometry output, while consuming relatively limited computing resources. This research provides a novel approach for the implementation of real-time lidar-IMU odometry.

LiDAR-based global 3D pose measurement via synthetic scan retrieval and hierarchical registration

Accurate pose measurement without an initial pose estimate is essential for mobile robots operating in GNSS-denied or structurally complex environments. However, global 3D relocalization from LiDAR measurements remains computationally demanding because direct registration over 3D prior maps involves a high-dimensional search space and dense point-cloud matching. To address these issues, this paper proposes a synthetic-scan-based hierarchical LiDAR measurement framework for fast 3D global relocalization. In the offline stage, feasible sensor positions are generated in a 3D occupancy grid using constraint-aware rapidly-exploring random tree-based uniform sampling, and at each sampled position, first-return ray casting is performed to generate a synthetic LiDAR scan whose descriptor is stored together with the corresponding position in the descriptor database. In the online stage, the retrieved 3D position and yaw are combined with nominal roll and pitch values to initialize iterative closest point (ICP)-based point-cloud registration. ICP subsequently refines the rigid transformation in SE(3) and outputs the final pose estimate. Real-world experiments demonstrate that the proposed method achieves pose-wise mean relocalization times of approximately 3 s or less and an average position error of 8 cm in 3D environments. Compared with conventional global registration and feature-matching baselines, the method substantially reduces online computation while maintaining high pose-measurement accuracy.

Simulation and Implementation of a 3D LiDAR SLAM and Autonomous Navigation System Based on Isaac Sim and Fast-LIO2

Autonomous mobile robot navigation in complex environments depends on high-precision mapping and reliable path planning. Traditional 2D LiDAR systems often suffer from height information loss, while wheel odometry is prone to significant drift in uneven or slippery terrain. This paper proposes a complete navigation solution from simulation verification to real-world implementation, which is particularly suitable for indoor semi-structured scenarios with uneven ground, slipping risks, or overhanging obstacles. We construct a simulation environment consistent with the real scene in NVIDIA Isaac Sim and use RTX-accelerated path tracing to simulate 3D LiDAR point clouds for algorithm validation. For robust localization, the Fast-LIO2 algorithm based on a tightly-coupled Iterative Extended Kalman Filter (IEKF) is used to replace traditional wheel odometry. The 3D point clouds are processed via Octomap for voxelization and projected into 2D occupancy grid maps to enable seamless integration with the ROS 2 Navigation2 (Nav2) stack. After verifying the algorithm flow in the simulation environment, we deployed the same architecture to a physical Mecanum wheel platform equipped with a Livox Mid-360 LiDAR. Experimental results demonstrated that the system worked stably in both simulated and real-world environments with good consistency and robustness. The proposed scheme has the potential to effectively shorten the development cycle and provide a reliable framework for 3D LiDAR-based autonomous navigation.

Automatic positioning of aircraft large components via LiDAR point cloud sensing

Lightweight 2.5D SLAM with Dynamic Map Refinement and Height-Aware Encoding for Resource-Constrained Indoor Robots

Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framework using a single-line laser distance sensor (LDS), time-of-flight (ToF) sensing, wheel odometry, and an inertial measurement unit (IMU). In this work, 2.5D refers to a 2D grid map with discretized vertical occupancy bins for each grid cell, rather than a full continuous 3D reconstruction. The system integrates multi-sensor synchronization, motion correction, error-state Kalman filter (ESKF)-based state estimation, normal distributions transform (NDT) registration, and pose graph optimization to reconstruct a pose-consistent global map. Based on this map, an offline dynamic refinement module estimates temporal voxel support across keyframes, extracts low-support candidate regions, and applies geometric clustering and isolated-point filtering to suppress transient residual artifacts while preserving stable structures. A 24-bit RGB occupancy encoding is further proposed to store the discretized vertical occupancy state in a compact three-channel image format. The proposed framework emphasizes system-level deployment value by combining sparse multi-sensor mapping, conservative offline refinement, and compact height-aware map export on a low-cost indoor robot platform. Experiments on public datasets, embedded hardware, and self-collected indoor sequences evaluate odometry reference performance, resource usage, platform-specific 2.5D mapping, dynamic refinement, and height-aware encoding.

A Multi-Sensor Fusion SLAM Method with Ground Constraints for Inspection Robots

This study proposes a multi-sensor fusion-based Simultaneous Localization and Mapping framework (LIOG-SLAM) to address positioning errors and drift issues encountered by substation inspection robots in large-scale substations. By integrating 3D LiDAR, Inertial Measurement Unit (IMU), Wheel Odometry (ODO), and Global Navigation Satellite System (GNSS) data, combined with ground-optimized odometry constraints, IMU-ODO joint pre-integration, sliding window marginalization, and loop closure detection, positioning accuracy and system robustness have been enhanced. In the KITTI dataset and Gazebo simulation environment, LIOG-SLAM demonstrated higher accuracy and lower error compared to traditional algorithms such as A-LOAM and LIO-SAM. Specifically, the root mean square error of APE decreased by 9.1 m and 5.207 m on the same sequence dataset, while maintaining efficient real-time performance in complex environments. This method offers a novel solution for efficient inspection in substations.