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3D point cloud-based place recognition is highly demanded by autonomousdriving in GPS-challenged environments and serves as an essential component(i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposesa novel approach, named NDT-Transformer, for realtime and large-scale placerecognition using 3D point clouds. Specifically, a 3D Normal DistributionTransform (NDT) representation is employed to condense the raw, dense 3D pointcloud as probabilistic distributions (NDT cells) to provide the geometricalshape description. Then a novel NDT-Transformer network learns a globaldescriptor from a set of 3D NDT cell representations. Benefiting from the NDTrepresentation and NDT-Transformer network, the learned global descriptors areenriched with both geometrical and contextual information. Finally, descriptorretrieval is achieved using a query-database for place recognition. Compared tothe state-of-the-art methods, the proposed approach achieves an improvement of7.52% on average top 1 recall and 2.73% on average top 1% recall on the OxfordRobotcar benchmark.

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation