A major challenge with off-road autonomous navigation is the lack of maps or
road markings that can be used to plan a path for autonomous robots. Classical
path planning methods mostly assume a perfectly known environment without
accounting for the inherent perception and sensing uncertainty from detecting
terrain and obstacles in off-road environments. Recent work in computer vision
and deep neural networks has advanced the capability of terrain traversability
segmentation from raw images; however, the feasibility of using these noisy
segmentation maps for navigation and path planning has not been adequately
explored. To address this problem, this research proposes an uncertainty-aware
path planning method, URA* using aerial images for autonomous navigation in
off-road environments. An ensemble convolutional neural network (CNN) model is
first used to perform pixel-level traversability estimation from aerial images
of the region of interest. The traversability predictions are represented as a
grid of traversal probability values. An uncertainty-aware planner is then
applied to compute the best path from a start point to a goal point given these
noisy traversal probability estimates. The proposed planner also incorporates
replanning techniques to allow rapid replanning during online robot operation.
The proposed method is evaluated on the Massachusetts Road Dataset, the
DeepGlobe dataset, as well as a dataset of aerial images from off-road proving
grounds at Mississippi State University. Results show that the proposed image
segmentation and planning methods outperform conventional planning algorithms
in terms of the quality and feasibility of the initial path, as well as the
quality of replanned paths.

面向离线环境的自主导航，提出了一种基于航空图像的不确定性感知路径规划方法（URA*），利用集成的卷积神经网络模型从区域感兴趣的航空图像中进行像素级可行性估计，然后应用不确定性感知规划器计算路径，并使用重规划技术来实现在线机器人操作中的快速重规划。结果表明，该方法在初始路径的质量和可行性及重新规划路径的质量方面优于传统规划算法。