Descriptellation: Deep Learned Constellation Descriptors for SLAM

• URL: http://arxiv.org/abs/2203.00567v1
• Date: Tue, 1 Mar 2022 15:43:01 GMT
• Title: Descriptellation: Deep Learned Constellation Descriptors for SLAM
• Authors: Chunwei Xing, Xinyu Sun, Andrei Cramariuc, Samuel Gull, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Florian Tschopp
• Abstract summary: We formulate a learning-based approach by constructing constellations from semantically meaningful objects. We demonstrate the effectiveness of our Deep Learned Constellation Descriptor (Descriptellation) on the Paris-Rue-Lille and IQmulus datasets.
• Score: 33.191251654815865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current global localization descriptors in Simultaneous Localization and Mapping (SLAM) often fail under vast viewpoint or appearance changes. Adding topological information of semantic objects into the descriptors ameliorates the problem. However, hand-crafted topological descriptors extract limited information and they are not robust to environmental noise, drastic perspective changes, or object occlusion or misdetections. To solve this problem, we formulate a learning-based approach by constructing constellations from semantically meaningful objects and use Deep Graph Convolution Networks to map the constellation representation to a descriptor. We demonstrate the effectiveness of our Deep Learned Constellation Descriptor (Descriptellation) on the Paris-Rue-Lille and IQmulus datasets. Although Descriptellation is trained on randomly generated simulation datasets, it shows good generalization abilities on real-world datasets. Descriptellation outperforms the PointNet and handcrafted constellation descriptors for global localization, and shows robustness against different types of noise.

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