An Entropy-Based Targetless Real-Time Radar-Lidar Point Cloud Alignment System for Smart Sensor Fusion

Autonomous systems pose unique challenges for sensor fusion applications. In multi-sensor scenarios, a real-time data geometric alignment system, from initial online calibration to instant data consistency evaluation, is highly desirable for effective and efficient deployment of autonomy solutions. In this paper, we present an entropy-based real-time geometric alignment system for Radar-Lidar point cloud sensor fusion. The online alignment system is targetless and relies solely on multisensor point cloud measurements to form an entroy-based test statistics, requiring no prior information about the perception environment. Specifically, we design a finite mixture model (FMM) as empirical probability density function (PDF) to represent environment as a probabilistic world model. A proper entropy measure of the empirical PDF according to the perception world is then introduced to evaluate the FMM randomness. It can be observed that, even in a generally nonstationary environment, both Radar and Lidar point clouds can still converge to an optimal entropy. The gradual fluctuation of this entropy measure over time can serve as a data consistency metric, enabling the detection of sudden sensor drifts. A scenario study is carried out to evaluate and validate the effectiveness and efficiency of the proposed real-time point-cloud alignment system in real world environments.