  Semantically interpreting the traffic scene is crucial for autonomous
transportation and robotics systems. However, state-of-the-art semantic
segmentation pipelines are dominantly designed to work with pinhole cameras and
train with narrow Field-of-View (FoV) images. In this sense, the perception
capacity is severely limited to offer higher-level confidence for upstream
navigation tasks. In this paper, we propose a network adaptation framework to
achieve Panoramic Annular Semantic Segmentation (PASS), which allows to re-use
conventional pinhole-view image datasets, enabling modern segmentation networks
to comfortably adapt to panoramic images. Specifically, we adapt our proposed
SwaftNet to enhance the sensitivity to details by implementing attention-based
lateral connections between the detail-critical encoder layers and the
context-critical decoder layers. We benchmark the performance of efficient
segmenters on panoramic segmentation with our extended PASS dataset,
demonstrating that the proposed real-time SwaftNet outperforms state-of-the-art
efficient networks. Furthermore, we assess real-world performance when
deploying the Detail-Sensitive PASS (DS-PASS) system on a mobile robot and an
instrumented vehicle, as well as the benefit of panoramic semantics for visual
odometry, showing the robustness and potential to support diverse navigational
applications.

Computer Vision and Pattern Recognition;Robotics;Image and Video Processing;Signal Processing