Polyline Based Generative Navigable Space Segmentation for Autonomous Visual Navigation

• URL: http://arxiv.org/abs/2111.00063v1
• Date: Fri, 29 Oct 2021 19:50:48 GMT
• Title: Polyline Based Generative Navigable Space Segmentation for Autonomous Visual Navigation
• Authors: Zheng Chen, Zhengming Ding, David Crandall, Lantao Liu
• Abstract summary: We propose a representation-learning-based framework to enable robots to learn the navigable space segmentation in an unsupervised manner. We show that the proposed PSV-Nets can learn the visual navigable space with high accuracy, even without any single label.
• Score: 57.3062528453841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments. In this work, we treat the visual navigable space segmentation as a scene decomposition problem and propose Polyline Segmentation Variational AutoEncoder Networks (PSV-Nets), a representation-learning-based framework to enable robots to learn the navigable space segmentation in an unsupervised manner. Current segmentation techniques heavily rely on supervised learning strategies which demand a large amount of pixel-level annotated images. In contrast, the proposed framework leverages a generative model - Variational AutoEncoder (VAE) and an AutoEncoder (AE) to learn a polyline representation that compactly outlines the desired navigable space boundary in an unsupervised way. We also propose a visual receding horizon planning method that uses the learned navigable space and a Scaled Euclidean Distance Field (SEDF) to achieve autonomous navigation without an explicit map. Through extensive experiments, we have validated that the proposed PSV-Nets can learn the visual navigable space with high accuracy, even without any single label. We also show that the prediction of the PSV-Nets can be further improved with a small number of labels (if available) and can significantly outperform the state-of-the-art fully supervised-learning-based segmentation methods.

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