Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference

• URL: http://arxiv.org/abs/2108.03180v1
• Date: Fri, 6 Aug 2021 15:51:40 GMT
• Title: Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference
• Authors: Aishwarya Unnikrishnan, Joseph Wilson, Lu Gan, Andrew Capodieci, Paramsothy Jayakumar, Kira Barton, Maani Ghaffari
• Abstract summary: We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a continuous (i.e., can be queried at arbitrary resolution) Bayesian model that propagates the scene with flow and infers a 3D semantic occupancy map with better performance than its static counterpart.
• Score: 3.0389083199673337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a continuous (i.e., can be queried at arbitrary resolution) Bayesian model that propagates the scene with flow and infers a 3D semantic occupancy map with better performance than its static counterpart. Experimental results using publicly available data sets show that the proposed framework generalizes its predecessors and improves over direct measurements from deep neural networks consistently.

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