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All Journal IAES International Journal of Robotics and Automation (IJRA)
Yang, Lixiao
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Automotive Engineering Electrical & Electronics Engineering

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ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance Yang, Lixiao; Feng, Youbing
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i4.pp399-408

Abstract

Focusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments.
ISTD-LIOM: Direct LiDAR-inertial odometry and mapping with intensity-enhanced stable triangle descriptor Yang, Lixiao; Hua, Sheng; Feng, Youbing; Yang, Shangzong; Wang, Jie
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp52-62

Abstract

To address the cumulative drift problem of light detection and ranging (LiDAR)-inertial odometry (LIO) in long-duration localization and mapping tasks, this paper proposes a LiDAR-inertial odometry and mapping system, intensity-enhanced stable triangle descriptor-LiDAR-inertial odometry and mapping (ISTD-LIOM), based on the intensity-enhanced stable triangle descriptor (ISTD). This system, built on the FAST-LIO2 front-end architecture, achieves global consistency localization through loop closure detection and global optimization. First, we design the ISTD descriptor by combining geometric descriptors of triangles (including vertex plane normal vectors and edge lengths) with local intensity distribution descriptors to form a compact, rotation-invariant feature representation. Next, an adaptive keyframe management mechanism is constructed, which filters keyframes based on inter-frame relative poses and generates a descriptor database. A hybrid retrieval strategy is then proposed, which combines descriptor similarity matching and spatial distance filtering, forming an efficient loop closure candidate recognition mechanism. After applying plane iterative closest point (ICP) refinement and geometric-intensity consistency validation, the loop closure constraints are integrated into a pose graph optimization framework, correcting odometry drift. Experiments on the KITTI dataset demonstrate that the ISTD-LIOM system significantly enhances map global consistency while maintaining real-time computational performance.
Co-Authors Feng, Youbing Hua, Sheng Wang, Jie Yang, Shangzong