A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration
This addresses the need for accurate sensor fusion in autonomous vehicles to improve robustness in inclement weather, representing an incremental advancement over existing 2D radar calibration methods.
The paper tackles the problem of calibrating 3D radar-to-camera sensors for autonomous vehicles by developing a continuous-time algorithm that uses radar velocity measurements and eliminates the need for specialized retroreflectors, demonstrating efficacy in synthetic and real-world experiments.
Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigid-body transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors - however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments.