M2DP: A novel 3D point cloud descriptor and its application in loop closure detection

By Li He, Xiaolong Wang, Hong Zhang. IEEE/RJS International Conference on Intelligent RObots and Systems, 2016

364 citations recorded by the source corpus.

How researchers see this paper

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MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, methodology): “In particular, LiDARbased place recognition methods [3, 14, 15, 2, 29, 30] have been widely studied to exploit the accuracy of direct 3D measurement for distant structures (e.”
Scan Context: Egocentric Spatial Descriptor for Place Recognition Within 3D Point Cloud Map (2018, methodology): “6 m 3 grid for both scan context and M2DP, since He et al. [10] reported M2DP is robust to downsampling, whereas Z-projection and ESF use an original point cloud without downsampling because they are vulnerable to low density.”
Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments (2021, methodology): “1) KITTI: KITTI Odometry 2 [12] is the most widely used dataset for LiDAR place recognition [25, 29, 32, 30, 31].”
Unsupervised Point Cloud Pre-training via Occlusion Completion (2020, methodology): “We compare OcCo with Jigsaw and two hand-crafted point cloud global descriptors: viewpoint feature histogram (VFH) [40] and M2DP [17] in Table 7.”