Sheng Bao
Hong Kong Polytechnic University
10 Papers
Sheng Bao is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Point cloud & Computer science. The author has an hindex of 1, co-authored 4 publications.
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Papers
Low-Drift Odometry, Mapping and Ground Segmentation Using a Backpack LiDAR System
Pengxin Chen,Wenzhong Shi,Sheng Bao,Muyang Wang,Wenzheng Fan,Haodong Xiang +5 more
- 14 Jul 2021
TL;DR: Li et al. as discussed by the authors proposed a framework for odometry, mapping and ground segmentation using a backpack LiDAR system that achieves both real-time and low-drift performance.
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FastLCD: A fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping
TL;DR: A fast and compact loop closure detection method (FastLCD) based on comprehensive descriptors and machine learning to achieve reliable and precise results using 3D point cloud for indoor LiDAR mobile mapping.
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Map-Assisted Seamless Localization Using Crowdsourced Trajectories Data and Bi-LSTM Based Quality Control Criteria
TL;DR: Experimental results prove that the proposed SL-SDBQ can achieve autonomous and precise indoor and outdoor positioning performance, and meter-level localization precision can be realized under the assistance of indoor map.
15
RectMatch: A novel scan matching method using the rectangle-flattening representation for mobile LiDAR systems
TL;DR: A squared point-to-rectangle distance function that is piecewise yet continuously differentiable to leverage the rectangle-flattening representation for scan matching and a clustering method based on density, direction and flattening that allows regions to grow in a “planes first, lines second, less flattened structures last” manner.
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DeLightLCD: A Deep and Lightweight Network for Loop Closure Detection in LiDAR SLAM
TL;DR: Li et al. as discussed by the authors proposed a very deep and lightweight neural network deep and light loop closure detection (DeLightLCD), which contains two key modules: 1) a Siamese feature extraction module and 2) a dual-attention-based feature difference module.
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