Panjie Lei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Panjie Lei has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chenxi Li
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Chuanwei Zhang
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Jiahao Li
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Jiazhong Li
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Sidong Yang
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication
Latest Publications
- Open Access
A Spatiotemporal Consistency-Enhanced Tightly Coupled LiDAR-Visual-Inertial SLAM Method for Complex Degraded Environments
Tightly coupled LiDAR-visual-inertial simultaneous localization and mapping (simultaneous localization and mapping, SLAM) systems are prone to observation distortion, weak state observability, and cumulative drift under weak-texture conditions, dynamic illumination, geometric degradation, multisensor time asynchrony, and long-term operation. To address these issues, we propose a spatiotemporal consistency enhancement method. The method is built upon the sequentially updated Error-State Iterated Kalman Filter (ESIKF) framework of FAST-LIVO2. First, using the Inertial Measurement Unit (IMU) clock as the unified reference, the LiDAR–IMU and Camera–IMU time offsets are jointly estimated online, and the estimates are fed back to point-cloud motion compensation and image reprojection. Second, through joint parameterization of vignetting, exposure time, and the camera response function, pixel intensity is inversely mapped to scene irradiance, while scale normalization and physical feasibility constraints are introduced to improve the identifiability of photometric parameters. Third, a LiDAR health indicator is established from the spectrum of a scale-normalized Hessian matrix, and direction-selective kinematic virtual observations are introduced under degeneration when motion is in a steady state and the innovation passes the gating threshold. Finally, low-frequency loop-closure constraints are generated using a ring-sector descriptor, rotation-invariant matching, and point-cloud geometric verification, while small-residual gating and progressive correction are adopted to reduce violation of the small-error assumption of ESIKF. Experimental results show that the proposed method achieves an overall RMSE of 0.379 m on the self-built visual and geometric degradation datasets; on the building-corridor loop-closure sequence, the average ATE decreases from 0.188 m with FAST-LIVO2 to 0.080 m; and in the unified ablation experiment, the average RMSE of the full system decreases from 0.153 m with FAST-LIVO2 to 0.117 m, a reduction of 23.5%. These results indicate that the enhancement modules are complementary in improving multisource observation consistency, degradation robustness, and long-term drift suppression.
Open AccessA Spatiotemporal Consistency-Enhanced Tightly Coupled LiDAR-Visual-Inertial SLAM Method for Complex Degraded Environments
Tightly coupled LiDAR-visual-inertial simultaneous localization and mapping (simultaneous localization and mapping, SLAM) systems are prone to observation distortion, weak state observability, and cumulative drift under weak-texture conditions, dynamic illumination, geometric degradation, multisensor time asynchrony, and long-term operation. To address these issues, we propose a spatiotemporal consistency enhancement method. The method is built upon the sequentially updated Error-State Iterated Kalman Filter (ESIKF) framework of FAST-LIVO2. First, using the Inertial Measurement Unit (IMU) clock as the unified reference, the LiDAR–IMU and Camera–IMU time offsets are jointly estimated online, and the estimates are fed back to point-cloud motion compensation and image reprojection. Second, through joint parameterization of vignetting, exposure time, and the camera response function, pixel intensity is inversely mapped to scene irradiance, while scale normalization and physical feasibility constraints are introduced to improve the identifiability of photometric parameters. Third, a LiDAR health indicator is established from the spectrum of a scale-normalized Hessian matrix, and direction-selective kinematic virtual observations are introduced under degeneration when motion is in a steady state and the innovation passes the gating threshold. Finally, low-frequency loop-closure constraints are generated using a ring-sector descriptor, rotation-invariant matching, and point-cloud geometric verification, while small-residual gating and progressive correction are adopted to reduce violation of the small-error assumption of ESIKF. Experimental results show that the proposed method achieves an overall RMSE of 0.379 m on the self-built visual and geometric degradation datasets; on the building-corridor loop-closure sequence, the average ATE decreases from 0.188 m with FAST-LIVO2 to 0.080 m; and in the unified ablation experiment, the average RMSE of the full system decreases from 0.153 m with FAST-LIVO2 to 0.117 m, a reduction of 23.5%. These results indicate that the enhancement modules are complementary in improving multisource observation consistency, degradation robustness, and long-term drift suppression.