Extrinsic Calibration of Radar Mount Position and Orientation with Multiple Target Configurations

Radars are part of the sensor suite installed on modern vehicles for environmental perception. The position and orientation of the radar must be known in order to transform the detections from the radar coordinate system to a vehicle coordinate system (VCS), which is a common requirement for multi-sensor fusion. In this work, 77GHz automotive radar sensors are extrinsically calibrated by registering the radar detections of corner reflector targets with known locations of the targets in the VCS; the procedure estimates the position and orientation parameters needed to transform radar detections onto the VCS. Radar detections are noisy and very sparse, hence, effort is put into achieving good calibration accuracy by taking advantage of multiple target configurations. Two multi-target methods are discussed; one which models estimation error as white noise and averages multiple estimates, another which combines all observations to make the data points denser for a one-time global estimation. The methods are tested with both synthetic and experimental data. The synthetic data result shows that, with sparse data points per target configuration, the estimation errors obtained due to the global method tend to decay faster than those obtained due to the averaging method as the number of configurations increases. The experimental data obtained from just 10 target configurations result in estimation errors of about 0.35° and 1cm for the orientation and position parameters, respectively