Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations

• URL: http://arxiv.org/abs/2203.01151v1
• Date: Wed, 2 Mar 2022 14:49:51 GMT
• Title: Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
• Authors: Frank Bieder, Maximilian Link, Simon Romanski, Haohao Hu, Christoph Stiller
• Abstract summary: We introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. For each representation a tailored deep learning architecture is developed to effectively extract semantic information.
• Score: 3.0413873719021995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.

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