Perception Framework of Water Hazards Beyond Traversability for Real-World Navigation Assistance Systems

Abstract

Traversability perception constitutes an important task for robotics and visually impaired people, which aims to detect obstacle-free paths that allow individuals to ambulate with suitable navigational aids. However, approaches that help prevent stepping into water areas are rare in state-of-art autonomous or assisted navigation systems. To address water hazard detection, this paper proposes a pRGB-D-SS perception framework, which incorporates: Polarization imaging (p), RGB-D sensory awareness and real-time Semantic Segmentation (SS). More specifically, as large water areas and small water puddles exhibit different characteristics, the detection of these two kinds of hazards pursue different pipelines. In our contribution, large water areas are detected together with traversable areas through pixel-wise semantic segmentation. Comparatively, the detection of water puddles extends the convolutional neural network based segmentation by using polarized RGB-D information as the primary cue. Beyond enhanced traversability awareness, it enables a unified framework of water hazard detection, which has been evaluated in a comprehensive variety of real-world surroundings on two wearable systems including a pair of commercial intelligent spectacles and a customized prototype, demonstrating the speeded-up efficiency and augmented sensorial/environmental robustness.