FREE-LIVO: Physically Decoupled Yet Tightly Coupled LiDAR-Inertial-Visual Odometry
Overview
This research presents FREE-LIVO, a tightly coupled odometry method that does not require a rigid LiDAR-camera rig or time synchronization. By using a pretrained image feature matcher to establish correspondences between camera images and dense LiDAR intensity images generated from a single scan, the method constructs reprojection factors to jointly optimize LiDAR and camera poses with IMU constraints. Tested on six sequences from the M3DGR dataset, FREE-LIVO achieves an average absolute trajectory error (ATE) RMSE of 0.45 m without extrinsic parameters and maintains accuracy under time synchronization errors up to 0.5 s. Experiments also show the method can jointly estimate trajectories for LiDAR and camera mounted on separate robots.
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Reported quantity · time synchronization errors: 0.5 other · Basis not reported
“al accuracy degradation under time synchronization errors of up to 0.5 s. Furthermore, experiments with the LiDAR and camera mounted on separate robots demonstrate that the method can jointly estimate the trajectories of independently moving sensors.”
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Reported quantity · absolute trajectory error RMSE: 0.45 other · Basis not reported
“different acquisition times. These factors are jointly optimized in a single factor graph together with constraints from point cloud registration, visual feature tracking, and the IMU measurements associated with each sensor. On six sequences from the public M3DGR dataset, FREE-LIVO achieves an average absolute trajectory error (ATE) RMSE of 0.45 m without provided LiDAR-camera extrinsic parameters and exhibits minim”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFREE-LIVO: Physically Decoupled Yet Tightly Coupled LiDAR-Inertial-Visual Odometry
This research presents FREE-LIVO, a tightly coupled odometry method that does not require a rigid LiDAR-camera rig or time synchronization. By using a pretrained image feature matcher to establish correspondences between camera images and dense LiDAR intensity images generated from a single scan, the method constructs reprojection factors to jointly optimize LiDAR and camera poses with IMU constraints. Tested on six sequences from the M3DGR dataset, FREE-LIVO achieves an average absolute trajectory error (ATE) RMSE of 0.45 m without extrinsic parameters and maintains accuracy under time synchronization errors up to 0.5 s. Experiments also show the method can jointly estimate trajectories for LiDAR and camera mounted on separate robots.
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