Adaptive point cloud acquisition and upsampling for automotive lidar

One of the crucial factors in achieving a higher level of autonomy of self-driving vehicles is a sensor capable of acquiring accurate and robust information about the environment and other participants in traffic. In the past few decades, various types of sensors have been used for this purpose, such as cameras registering visible, near-infrared, and thermal parts of the spectrum, as well as radars, ultrasonic sensors, and lidar. Due to their high range, accuracy, and robustness, lidars are gaining popularity in numerous applications. However, in many cases, their spatial resolution does not meet the requirements of the application. To solve this problem, we propose a strategy for better utilization of the available points. In particular, we propose an adaptive paradigm that scans the objects of interest with increased resolution, while the background is scanned using a lower point density. Initial region proposals are generated using an object detector that relies on an auxiliary camera. Such a strategy improves the quality of the representation of the object, while retaining the total number of projected points. The proposed method shows improvements compared to regular sampling in terms of the quality of upsampled point clouds.