3D Object Recognition and Localization with a Dense LiDAR Scanner

Dense scanning is an effective solution for refined geometrical modeling applications. The previous studies in dense environment modeling mostly focused on data acquisition techniques without emphasizing autonomous target recognition and accurate 3D localization. Therefore, they lacked the capability to output semantic information in the scenes. This article aims to make complementation in this aspect. The critical problems we solved are mainly in two aspects: (1) system calibration to ensure detail-fidelity for the 3D objects with fine structures, (2) fast outlier exclusion to improve 3D boxing accuracy. A lightweight fuzzy neural network is proposed to remove most background outliers, which was proven in experiments to be effective for various objects in different situations. With precise and clean data ensured by the two abovementioned techniques, our system can extract target objects from the original point clouds, and more importantly, accurately estimate their center locations and orientations.

Automated summary

This article introduces an effective solution for dense scanning, focusing on autonomous target recognition and accurate 3D localization in the process of geometrical modeling. By employing system calibration and fast outlier exclusion techniques, precise and clean data can be obtained, enabling the extraction of target objects and accurate estimation of their positions and orientations.