Development and Evaluation of a Reconfigurable 3D LiDAR Sensor for Improved Perception in Autonomous Systems

Abstract

Light detection and ranging (LiDAR) is widely used in robotics, autonomous vehicles, remote sensing, and object tracking, and a large number of commercial 3D LiDAR sensors with diverse specifications are now available. However, these sensors are typically sold with fixed specifications: their measuring range, field of view (FoV), angular resolution, number of scan points, and number of scan layers are all determined at the point of manufacture and cannot be adapted to the application. This rigidity forces the surrounding platform to absorb the cost of excess data, higher computation, larger post-processing storage, and false feature detections even when only a narrow region of interest is needed. To address these limitations, this paper presents 3D Customizable LiDAR (3D CS LiDAR), a novel reconfigurable 3D LiDAR architecture that exposes sensor specifications as run-time parameters. The paper describes the mechanical, electrical, and software subsystems of the developed sensor and evaluates its object-detection performance against a commercial 32-channel LiDAR under two experimental setups. Within the scope of the indoor evaluation reported here (1–3 m range, three geometric targets), the proposed reconfigurable 3D LiDAR provides denser on-target sampling and lower false negative rates than the commercial reference sensor in every tested configuration, indicating its potential for close-range indoor perception tasks such as robotic inspection and short-range obstacle detection. This work contributes to LiDAR technology by introducing a run-time-reconfigurable approach that addresses the specification rigidity of existing sensors and outlines the initial progress towards a full-fledged reconfigurable 3D LiDAR system, with outdoor operation and long-range characterisation identified as future work.