A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping

Author:

Wei Peng1ORCID,Fu Kaiming2ORCID,Villacres Juan1ORCID,Ke Thomas3ORCID,Krachenfels Kay3ORCID,Stofer Curtis Ryan3,Bayati Nima3,Gao Qikai4,Zhang Bill4ORCID,Vanacker Eric4,Kong Zhaodan4ORCID

Affiliation:

1. Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA

2. Department of Electrical and Computer Engineering, University of California, Davis, CA 95616, USA

3. Department of Computer Science, University of California, Davis, CA 95616, USA

4. Department of Mechanical and Aerospace Engineering, University of California, Davis, CA 95616, USA

Abstract

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

Funder

National Science Foundation

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Feasibility study on fruit parameter estimation based on hyperspectral LiDAR point cloud;Journal of Food Measurement and Characterization;2024-07-10

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