A Low-Cost 3D Rigid Object Posture Estimation Using 2D LiDAR Dynamics Elevation Mapping System for Autonomous Vehicle

Abstract

This research presents the development of a low-cost 3D rigid-object posture estimation system using a 2D LiDAR sensor with dynamic elevation mapping for autonomous vehicle applications. The methodology encompasses four key components: (1) the development of a data acquisition system that integrates a 2D LiDAR with a servo-controlled elevation mechanism, enabling precise vertical scanning; (2) the implementation of coordinate transformation algorithms to reconstruct accurate 3D point clouds from sequential 2D scans; (3) the optimization of point cloud density through multi-parameter approaches, including adaptive scan resolution and noise filtering; and (4) the application of the Iterative Closest Point (ICP) algorithm for precise object posture estimation, ensuring robust alignment between observed and reference point clouds. The system is designed to enhance perception in autonomous driving by providing real-time, high-accuracy 3D pose estimation while maintaining affordability and computational efficiency. The system's performance was evaluated through testing in two distinct environments: an indoor laboratory setting (Room PS 03.07) and a corridor space (SAW Building) at PENS campus Sukolilo, demonstrating the system's capability to generate accurate 3D representations with color-coded elevation mapping ranging from 0.00 to 8.48 meters, while the point cloud optimization achieved efficient data compression through a voxel grid filter with 4mm leaf size, ensuring optimal point density between 4-8mm minimum point distances, successfully detecting and mapping various rigid objects while maintaining geometric accuracy in both confined and extended spaces. The results demonstrate cost-effective implementations by employing alternative sensing systems rather than relying on 3D LiDAR.