Problem
Research questions and friction points this paper is trying to address.
Extrinsic Calibration
Radar-LiDAR-Camera
Target-less
Online Calibration
Sensor Fusion
Online Target-less Radar-LiDAR-Camera Extrinsic Calibration via Joint Optimization
🤖 AI Summary
This study addresses the challenge of online, targetless extrinsic calibration among radar, LiDAR, and cameras, where existing methods struggle to ensure three-sensor consistency due to sparse and noisy radar data. To overcome the limitations of pairwise independent calibration, this work proposes a joint optimization framework that constructs residuals for each sensor pair and simultaneously optimizes all extrinsic parameters. Furthermore, robustness is enhanced through a distance-threshold-based adaptive radar denoising strategy combined with cross-frame correspondence accumulation. Evaluated on a self-collected urban dataset, the proposed method significantly reduces calibration errors across all sensor pairs, outperforming state-of-the-art baselines.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Target-less Extrinsic Calibration
Joint Optimization
Radar-LiDAR-Camera Fusion
Adaptive Noise Filter
Correspondence Accumulation
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