Loop-Closure Detection Based on 3D Point Cloud Learning for Self-Driving Industry Vehicles
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Self-driving industry vehicle plays a key role in the industry automation and contributes to resolve the problems of the shortage and increasing cost in manpower. Place recognition and loop-closure detection are main challenges in the localization and navigation tasks, specially when industry vehicles work in large-scale complex environments, such as the logistics warehouse and the port terminal. In this paper, we resolve the loop-closure detection problem by developing a novel 3D point cloud learning network, an active super keyframe selection method and a coarse-to-fine sequence matching strategy. More specifically, we first propose a novel deep neural network to extract a global descriptors from the original large-scale 3D point cloud, then based on which, an environment analysis approach is presented to investigate the feature space distribution of the global descriptors and actively select several super keyframes. Finally, a coarse-to-fine sequence matching strategy, which includes a super keyframe based coarse matching stage and a local sequence matching stage, is presented to ensure the loop-closure detection accuracy and real-time performance simultaneously. The proposed network is evaluated in different datasets and obtains a substantial improvement against the state-of-the-art PointNetVLAD in place recognition tasks. Experiment results on a self-driving industry vehicle validate the effectiveness of the proposed loop-closure detection algorithm.
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