6 papers · 1 filter
SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames
Yuxuan Zhou, Xingxing Li, Shengyu Li +3
For high-level geo-spatial applications and intelligent robotics, accurate global pose information is of crucial importance. Map-aided localization is a universal approach to overc…
iKalibr-RGBD: Partially-Specialized Target-Free Visual-Inertial Spatiotemporal Calibration For RGBDs via Continuous-Time Velocity Estimation
Shuolong Chen, Xingxing Li, Shengyu Li +1
Visual-inertial systems have been widely studied and applied in the last two decades (from the early 2000s to the present), mainly due to their low cost and power consumption, smal…
Equivariant Filter for Tightly Coupled LiDAR-Inertial Odometry
Anbo Tao, Yarong Luo, Chunxi Xia +2
Pose estimation is a crucial problem in simultaneous localization and mapping (SLAM). However, developing a robust and consistent state estimator remains a significant challenge, a…
RIs-Calib: An Open-Source Spatiotemporal Calibrator for Multiple 3D Radars and IMUs Based on Continuous-Time Estimation
Shuolong Chen, Xingxing Li, Shengyu Li +2
Aided inertial navigation system (INS), typically consisting of an inertial measurement unit (IMU) and an exteroceptive sensor, has been widely accepted as a feasible solution for…
iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems
Shuolong Chen, Xingxing Li, Shengyu Li +2
The integrated inertial system, typically integrating an IMU and an exteroceptive sensor such as radar, LiDAR, and camera, has been widely accepted and applied in modern robotic ap…
DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping
Yuxuan Zhou, Xingxing Li, Shengyu Li +3
Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability.…