aiqbench analysis
This research advances autonomous vehicle perception by solving the critical challenge of maintaining consistent extrinsic calibration across radar, LiDAR, and camera sensors without requiring external targets. The proposed joint optimization framework specifically addresses the unreliability of sparse radar data through adaptive noise filtering and correspondence accumulation, ensuring robust sensor fusion in adverse conditions. By minimizing overall residuals simultaneously for all sensor pairs, this method improves upon existing pairwise baselines essential for reliable 3D perception systems.
Online Target-less Radar-LiDAR-Camera Extrinsic Calibration via Joint Optimization
arXiv:2610.04552v1 Announce Type: new Abstract: Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate extrinsic calibration among the three sensors. In this paper, we address the problem of online target-less extrinsic calibration for the radar-LiDAR-camera system. Existing target-less methods are mostly designed for a single sensor pair, and composing the pairwise results does not guarantee consistency across the three sensors. Moreover, the sparse and noisy radar measurements make the radar-involving pairs unreliable. To tackle these challenges, we propose a joint calibration framework that constructs residuals for each sensor pair and optimizes the extrinsics of all pairs together to minimize the overall residual. Furthermore, we introduce an adaptive radar noise filter that rejects spurious radar returns using a range-dependent margin, and a correspondence accumulation strategy that aggregates sparse radar correspondences over frames. We validate our method on an in-house radar-LiDAR-camera dataset covering diverse urban environments, where it reduces calibration errors across all sensor pairs over a state-of-the-art camera-LiDAR baseline.
Abstract:Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate extrinsic calibration among the three sensors. In this paper, we address the problem of online target-less extrinsic calibration for the radar-LiDAR-camera system. Existing target-less methods are mostly designed for a single sensor pair, and composing the pairwise results does not guarantee consistency across the three sensors. Moreover, the sparse and noisy radar measurements make the radar-involving pairs unreliable. To tackle these challenges, we propose a joint calibration framework that constructs residuals for each sensor pair and optimizes the extrinsics of all pairs together to minimize the overall residual. Furthermore, we introduce an adaptive radar noise filter that rejects spurious radar returns using a range-dependent margin, and a correspondence accumulation strategy that aggregates sparse radar correspondences over frames. We validate our method on an in-house radar-LiDAR-camera dataset covering diverse urban environments, where it reduces calibration errors across all sensor pairs over a state-of-the-art camera-LiDAR baseline.
6 pages, 2 figures, 3 tables. Accepted to the International Conference on Control, Automation and Systems (ICCAS 2026)
Robotics (cs.RO)
arXiv:2610.04552 [cs.RO]
(or arXiv:2610.04552v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.04552 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 276 words
Related coverage
LV-Calib: LiDAR-Camera Extrinsic Calibration with Boundary-Response Modeling
arXiv · Jun 16, 2026
Accurate Measurement of 3D and 2D Circular Centers With Application to LiDAR-Camera Extrinsic Calibration
arXiv · Aug 28, 2026
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
arXiv · Sep 9, 2026
Elon Musk’s heterodox robotaxi philosophy gets put to the test
The Verge · Sep 2, 2026