Work

Camera–LiDAR Wide Range Calibration in Traffic Surveillance Systems

Year: 2025

Type: article

Abstract: In traffic surveillance systems, accurate camera-LiDAR calibration is critical for effective detection and robust environmental recognition. Due to the significant distances at which sensors are positioned to cover extensive areas and minimize blind spots, the calibration search space expands, increasing the complexity of the optimization process. This study proposes a novel target-less calibration method that leverages dynamic objects, specifically, moving vehicles, to constrain the calibration search range and enhance accuracy. To address the challenges of the expanded search space, we employ a genetic algorithm-based optimization technique, which reduces the risk of converging to local optima. Experimental results on both the TUM public dataset and our proprietary dataset indicate that the proposed method achieves high calibration accuracy, which is particularly suitable for traffic surveillance applications requiring wide-area calibration. This approach holds promise for enhancing sensor fusion accuracy in complex surveillance environments.

Source: Sensors

DOI: 10.3390/s25030974

Authors: Byung-Jin Jang, Taek-Lim Kim, Tae-Hyoung Park

Institution: Chungbuk National University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137