Collocated sensors bias estimation in autonomous driving
Kaipei Yang, Yaakov Bar‐Shalom, Peter Willett, Shawn D. Hunt · 2022
Sensors are prone to biases which can lead to inaccurate associations and hence poor results in target tracking. The sensors used on autonomous vehicles (AV) are placed together or very close (practically collocated) which makes the bias estimation challenging. This work considers the bias estimation for two collocated synchronized sensors with slowly varying additive biases. The biases’ observability condition is met when the two sensors’ biases are Ornstein-Uhlenbeck stochastic processes with different time constants. The proposed bias estimation is independent of state estimation and bias models are identified based on sample autocorrelations. With bias-compensated observations, the fused measurement can be obtained using the Maximum Likelihood fusion technique. In experiments, two collocated lidars (different manufacturer models) are tested in real time. It is shown that the uncertainties of biases are significantly reduced by the estimation algorithm presented. The observation error reduction is up to 77% with bias-compensated measurement fusion and the bias uncertainty (root mean square error) has reduction up to 45% after fusion compared to the single lidar scenario.