Title: A novel multi-sensor fusion approach for enhanced navigation in autonomous driving

Authors: Qinghai Liao; Feiyang Cheng; Ji Yu; Zhengguang Ao; Zhiquan Deng; Liang Huang; Huiyun Li

Addresses: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen – 518055, China; Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Research and Development Department, Minieye Technology Co., Ltd, Shenzhen – 518049, China ' Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen – 518055, China; Shenzhen University of Advanced Technology, Shenzhen – 518055, China

Abstract: The limitations of single-sensor SLAM technologies in addressing the intricate requirements of modern intelligent vehicles have prompted a shift towards multi-sensor fusion SLAM as a prominent area of research. In response, this paper proposes a tightly-coupled SLAM system integrating LiDAR, cameras, and IMUs to boost the location accuracy and mapping capabilities. The system processes multi-sensor data upfront to enable effective backend optimisation. Specifically, it integrates LiDAR odometry directly within the vision-inertial framework as inter-frame constraints to streamline computational complexity. Moreover, to counter the progressive error accumulation typical of odometry-based methods, loop closure detection is incorporated, enhancing the quality of localisation and mapping. The effectiveness is substantiated through experiments on public datasets, confirming its proficiency in accurate positioning and navigation. The experimental results demonstrate that the proposed multi-sensor fusion SLAM system maintains high accuracy and reliability across different speeds and environmental conditions, with improvements in trajectory estimation due to loop closure.

Keywords: autonomous driving; SLAM; multi-sensor fusion; pose estimation; LiDAR odometry.

DOI: 10.1504/IJIIDS.2026.155291

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.3/4, pp.289 - 307

Received: 17 May 2024
Accepted: 22 Sep 2024
Published online: 30 Jul 2026 *

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