Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images

• URL: http://arxiv.org/abs/2109.07245v1
• Date: Wed, 15 Sep 2021 12:25:56 GMT
• Title: Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
• Authors: Galadrielle Humblot-Renaux, Letizia Marchegiani, Thomas B. Moeslund and Rikke Gade
• Abstract summary: This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. We segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. We present a generic and scalable affordance-based definition consisting of 3 driveability levels which can be applied to arbitrary scenes.
• Score: 25.350677396144075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work tackles scene understanding for outdoor robotic navigation, solely relying on images captured by an on-board camera. Conventional visual scene understanding interprets the environment based on specific descriptive categories. However, such a representation is not directly interpretable for decision-making and constrains robot operation to a specific domain. Thus, we propose to segment egocentric images directly in terms of how a robot can navigate in them, and tailor the learning problem to an autonomous navigation task. Building around an image segmentation network, we present a generic and scalable affordance-based definition consisting of 3 driveability levels which can be applied to arbitrary scenes. By encoding these levels with soft ordinal labels, we incorporate inter-class distances during learning which improves segmentation compared to standard one-hot labelling. In addition, we propose a navigation-oriented pixel-wise loss weighting method which assigns higher importance to safety-critical areas. We evaluate our approach on large-scale public image segmentation datasets spanning off-road and urban scenes. In a zero-shot cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.

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