Mastering Data Complexity for Autonomous Driving with Adaptive Point Clouds for Urban Environments

Abstract

LiDAR sensors play a crucial role in autonomous driving and advanced driver assistance systems. By firing high-rate laser beams, a LiDAR device is able to project its surroundings as 2D or 3D point cloud, which can be used for different purposes such as object detection, map generation, localization, and navigation. Autonomous vehicles are often equipped with at least one multi-layer LiDAR sensor with 360-degree coverage to include as much information as possible in the point cloud. Such a device generates enormous amount of data which poses a challenge for data storage, real-time computation, and data transmission, as autonomous vehicles are typically resource-constrained systems. This paper proposes a lightweight and adaptive point cloud data structure to reduce the size of a 3D point cloud. The suggested data structure can be flexibly configured with different parameters to adapt for precision, distance coverage, and reflectivity resolution. The precision of the data structure is evaluated using a 16-layer Velodyne LiDAR sensor (VLP-16) to collect data in the city area of AstaZero proving ground and Gothenburg downtown. Our results show that the adaptive data structure can consume only 1/8th of the original point cloud size and hence, it is particularly suitable for applications with limited hardware resources or certain tolerance to precision of the point cloud. The suggested concept is also generalizable to other types of point cloud providing sensors.

Publication
Proceedings of the 28th IEEE Intelligent Vehicles Symposium (IVS)
Date
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