Abstract
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.
Highlights
With the rapid development of LiDAR technology, it has become the primary environment modeling tool to obtain the 3D geometry of a largescale space in the form of point clouds
The physical interpretation is: the distance of the section is far away enough from the object frontside; the point number is small, so it should not be on the object; the change of the point number with its previous neighbor is small, so it is in a stable range; the point height is low, so it is possible to be the ground
This paper presents a dense scanning system that recognizes and localizes ta
Summary
With the rapid development of LiDAR (light detection and ranging) technology, it has become the primary environment modeling tool to obtain the 3D geometry of a largescale space in the form of point clouds. For the constraints of plane flatness, the point clouds belonging to the same plane are fitted with a function of a plane, and the fitting error is used as the calibration cost. Both the rotational and translational corrections of the beam coordinate systems are minor, which is reasonable considering the small Actuators 2022, 11, x FOR PEER RdEiVmIEeWnsions of the light components in the scanner, the improvement for the environmen10taolf 33 modeling accuracy is evident. With all 6 DOF constraints restrained in the cost function, the internal parameters and external parameters are combined in the model to be calibrated It takes 30 min to optimize with 64,164 points in the data, which is acceptable because it is only done once offline. There are still significant differences between a camera image and a Rev
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