Spatiotemporal Alignment and Measurement Accuracy Evaluation of New Point Cloud Devices in Autonomous Driving

Despite the increasing applications of point cloud, the measurement accuracy evaluation of point cloud devices is usually neglected, causing precision loss of missions. This paper is based on the synchronization and extrinsic calibration of different modalities of sensors, obtaining spatiotemporal alignment accuracy using the cumulative distribution function, and the measurement model for integrity monitoring. After spatial alignment, we proposed the static and dynamic measurement accuracy evaluation approaches. The static accuracy evaluation is based on standstill landmarks and sensors, using the ground truth obtained by integrated navigation to acquire measurements error. The dynamic accuracy evaluation requires the sensor moving with a trajectory, and establishing the surroundings’ map using SLAM process as the ground truth, then the sensor measurement error could be evaluated. Experiments of new point cloud devices such as high-resolution solid-state LiDAR and low resolution 4D radar demonstrate the effectiveness of our method.