Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 32 references
Abstract
Loop Closure Detection is a fundamental component of any SLAM system. By performing place recognition, a robot can correct accumulated drift errors arising from odometry uncertainties during the mapping process. Numerous techniques have been proposed in the literature to address this task under different sensor configurations, including RGB cameras and LiDAR. Among these, LiDAR-based SLAM has gained substantial attention due to its robustness in outdoor environments and its invariance to illumination changes. However, LiDAR sensors inherently provide less texture information compared to cameras, introducing additional challenges for loop closure detection. One of the most widely adopted approaches in LiDAR-based SLAM is Scan Context, recognized for its simplicity and effectiveness. This method has been successfully integrated into a broad range of applications and algorithms. Nevertheless, its simplicity can also lead to reduced robustness when no supplementary verification mechanisms are employed. In this work, we introduce Gaussian Scan Context, an enhancement to the original Scan Context that incorporates statistical analysis of the input point cloud. This approach accounts for the distribution of points within each context bin. Experimental results demonstrate that this enhancement improves both robustness and overall performance, as supported by various metrics.
DZLoop is proposed, a novel LiDAR-based loop closure detection method that leverages multiple structural density images and Zernike moments that generates a robust global descriptor that effectively captures diverse structural characteristics of the environment.
Doyeon Kim, Heoncheol Lee· Intelligent Service Robotics· 0 citations
Simultaneous Localization and Mapping (SLAM) is a core technology for indoor mobile LiDAR mapping systems. In GNSS-denied environments, localization accuracy is often degraded by cumulative drift. Loop closure detection remains challenging under large viewpoint variations and limited scan overlap. To address this challenge, this paper proposes a loop closure detection method based on Key Plane Structures (KPS). Unlike point-based methods that are sensitive to sensor scanning angles, the proposed method exploits the geometric stability of indoor plane structures to construct invariant global descriptors. This representation improves the robustness of loop closure detection across different viewpoints and provides more reliable loop closure constraints for pose graph optimization. Extensive experiments on real-world datasets validate the effectiveness of the method. In scenarios with large viewpoint variations, the method reduces the absolute trajectory translation error to 0.248 m, significantly outperforming Scan Context. In scenarios with sufficient scan overlap, it achieves centimeter-level positioning accuracy. Overall, the proposed method reduces trajectory drift by approximately 68%–97% compared to the pure odometry baseline, demonstrating stable performance in indoor LiDAR SLAM.
Men Sha, Wenguang Wang, Changshun Yuan et al.· Engineering Research Express· 0 citations
Multisensor odometry combines LiDAR, visual and inertial measurements to provide accurate local motion estimation, but accumulated drift still degrades long-term trajectory and map consistency. Introducing an online loop-closure backend raises two practical concerns. Incorrect constraints may corrupt global optimisation, while direct application of backend corrections may cause discontinuities in the published pose. This paper presents an online robust loop-closure correction method for LiDAR--visual--inertial odometry without modifying the front-end estimator. LiDAR submaps provide the geometric evidence for loop registration, whereas the fused visual--inertial--LiDAR trajectory supports candidate retrieval, motion-consistency evaluation and online pose correction. Candidate constraints are generated by complementary global and local retrieval, verified by registration and statistical tests, and activated only after consistency with the odometric chain, agreement among induced corrections and observation support are established. The optimised correction is then released progressively in $SE(3)$. Experiments on 28 sequences from HILTI, M2DGR, MARS-LVIG and NTU-VIRAL reduce mean APE RMSE by 13.81\%, 8.72\%, 8.14\% and 2.09\%, respectively. Ablation results show that statistical and consistency screening suppress excessive loop activations, while progressive output reduces peak single-step translation and rotation changes by 59.6\% and 67.3\%. The corrected pose remains available at approximately 10~Hz.
LiDAR localization and mapping play an important role in mobile robotics and autonomous driving systems, but points from dynamic objects in urban environments can be incorrectly associated with the local map during scan-to-map registration, introducing erroneous constraints into pose estimation and thereby degrading localization accuracy. Existing occupancy-based dynamic object removal (DOR) methods typically face two challenges: insensitivity to overlapped dynamic regions and high sensitivity to occlusions. To address these issues, we propose a LiDAR-inertial localization framework with plug-and-play DOR. The DOR module removes dynamic points before registration, thereby reducing such erroneous constraints while preserving reliable static structures for more stable scan-to-map associations. It combines occupancy difference detection, connectivity-based region completion, and spatiotemporal overlap discrepancy to improve the completeness of dynamic region detection in overlapped regions and reduce the false removal of static points caused by occlusions. In addition, a multilayer consistency check based on Kullback–Leibler (KL) divergence is introduced during scan matching to reject surface constraints with inconsistent distributions, thereby improving data association reliability. The proposed method is evaluated on the KITTI, ECMD, UrbanNav, UrbanLoco, and a self-collected campus dataset. Compared with representative SOTA baselines, including VoxelMap, PV-LIO, TRLO, Dynamic-LIO, and BTSA, our method achieves the lowest average absolute trajectory error (ATE) among the compared methods, with 1.91 m on KITTI and 1.41 m on the selected ECMD comparison subset, demonstrating improved localization accuracy across environments with different scales and dynamic levels.
Nuo Li, Yiqing Yao, Xiaosu Xu et al.· IEEE Transactions on Instrum...· 0 citations
Dense partial-view LiDAR observations are attractive for outdoor perception, but limited overlap and viewpoint sensitivity make odometry and mapping less reliable than with spinning LiDARs. Many recent algorithms for this sensing regime are built as LiDAR-inertial odometry frameworks, whose localization and mapping performance can degrade or fail when the IMU state estimation becomes unstable. This paper presents a LiDAR-only framework for complex outdoor scenes using a factor-graph back-end. After denoising and motion compensation, the point cloud is projected onto a range image for ground, planar, edge, and line extraction. Pose estimation is strengthened by degeneracy-aware feature selection, while loop closing combines scan-based and path-based cues to handle partial-view revisits. Experiments in tunnels, urban roads, residential areas, and other challenging scenes show reduced drift and improved mapping consistency for dense limited-FoV LiDAR data.
Xin-Ye Dai, Ding-Xi Wang, Jin Xing et al.· Remote Sensing· 0 citations
Abstract. Localisation remains a crucial aspect of robotic design. It forms the basis of any kind of autonomous navigation for drones, cars and other specialized robots. This is usually achieved using a Simultaneous Localisation and Mapping (SLAM) algorithm, which uses an input sensor to localise the robot within a map that is created simultaneously. The input sensors are either cameras, which provide visual data, or Light Detection And Ranging (LiDAR) sensors, which automatically deliver a point cloud up to surveying quality. In recent years, LiDAR inertial odometry (LIO) algorithms, which combine measurements from a LiDAR sensor and inertial measurements from an IMU, have become more popular. These algorithms do not use a previously recorded map, but rather create their own map during runtime. This paper contributes an improvement to the precision by integrating a predefined 3D global point cloud map into the localisation algorithm. Over the course of multiple experiments in different testing scenarios, we have achieved a 71% reduction of the distance error for localisation, while there was no significant change regarding the orientation error. This makes the presented system a suitable localisation option for real-world robotic operations at construction sites.
Martin Hesse, A. Nuechter· The International Archives o...· 0 citations
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