Enhanced Object Recognition in Autonomous Driving through LiDAR Point Cloud Data Compression

Abstract

In autonomous driving, lidar-derived point cloud data is crucial for detecting objects and understanding the vehicle's environment. However, the large volume of raw data poses challenges in storage, transmission, and real-time processing. Compression of lidar point cloud data is vital, as it reduces data size without significantly losing geometric and semantic information, thus enhancing autonomous driving system performance. Compressed data enables swift and accurate object recognition, improving safety and reliability. This chapter examines current compression methodologies, their strengths, limitations, and applications. It focuses on vehicle recognition as a practical application, detailing the chosen algorithm's processing and compression of lidar data. The impact of compression on vehicle recognition accuracy and efficiency is discussed, with implications for intelligent transportation systems and future advanced compression techniques for real-time data processing in autonomous vehicles.