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.
With the rapid development of intelligent driving technology, Intelligent Parking Assist systems have been widely used. Through analyzing the technical characteristics of the Intelligent Parking Assist system, this article brings up subjective evaluation indicators of Intelligent Parking Assist system from the perspective of consumers' daily use; Practical verification is carried out for three typical parking scenarios including parallel parking spaces, vertical parking spaces and inclined train spaces, thus a set of subjective evaluation methods suitable for Intelligent Parking Assist systems for passenger cars is summarized.
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