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

Submitted:

18 June 2026

Posted:

22 June 2026

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Abstract
Light detection and ranging (LiDAR) sensors are widely known for their applications in 1 robotics, autonomous cars, remote sensing, and object tracking. These compact sensors are favored by 2 automation technologists for their accurate and long-range object detection capabilities. As a result, 3 a multitude of LiDAR sensors with diverse specifications have been introduced in the commercial 4 market. However, existing 3D LiDAR sensors lack the ability to customize their specifications, such 5 as measuring range, Field of View (FoV), angular resolution, number of scan points, and number of 6 scan layers, according to specific applications. This limitation poses several challenges for engineers, 7 including processing excessive data, requiring high computation power, demanding more storage 8 space for post-processing, and leading to false feature detection. To address these issues, this paper 9 presents a novel reconfigurable 3D LiDAR technology called 3D Customizable LiDAR (3D CS LiDAR), 10 which offers customization of sensor specifications based on the application requirements. The paper 11 discusses the mechanical and electrical systems of the developed LiDAR sensor and evaluates its 12 object detection performance by comparing it with a commercially available sensor. The results 13 demonstrate that the proposed reconfigurable 3D LiDAR system outperforms the commercial sensor 14 in all considered scenarios, indicating its potential for various perceptional applications. This research 15 contributes to the advancement of LiDAR technology by introducing a customizable approach, 16 addressing the limitations of existing sensors. The findings showcase the initial progress made 17 towards developing a comprehensive reconfigurable 3D LiDAR system, which holds promise for 18 diverse practical applications.
Keywords: 
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Subject: 
Engineering  -   Other
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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