Dynamic Obstacle Avoidance System Based on Rapid Instance Segmentation Network
Hongmin Mu, Gang Zhang, Zhe Ma, MengChu Zhou, Zhengcai Cao
Abstract
To assist the Partially Sighted and Visually Impaired (PSVI), a variety of Obstacle Avoidance (OA) methods have been developed. These methods mostly use depth cameras for distance measurement in terms of perception, and communicate to users through voice broadcasts. However, due to insufficient detection accuracy and slow system response, they are difficult to apply to narrow and multi-pedestrian areas. To overcome this difficulty, this work aims to develop a dynamic OA system using an improved instance segmentation network for high-precision detection. To improve the segmentation accuracy of accessible paths for PSVI users, it proposes a new 2D convolution unit that couples multi-scale receptive fields of deep features. This unit focuses on the global context of an input image by constructing a hierarchical residual-like structure. To improve the efficiency of exploration, this work adopts a bidirectional A* algorithm with safety distance constraints to plan optimal paths for PSVI users, thus avoiding their trial-and-error path finding. To ensure safety, it proposes a collision avoidance algorithm based on regional safety analysis, which can generate and transmit timely vibration response to users. Experimental results demonstrate that our developed system can help PSVI users to pass through those challenging areas safely and effectively.