Depth sensors like RGB-D cameras, LiDARs and laser scanners are widely investigated in research for Smart Wheelchair (SW) to carry out navigation, localization and obstacle detection and avoidance tasks. These sensors are costly compared to monocular camera sensor. A single off-the-shelf camera can be an economically efficient sensor to achieve obstacle detection and avoidance. We present in this paper a single camera based obstacle detection and avoidance method without using any 3D information. It is a novel vision-only system for wheelchair obstacle detection and avoidance that uses a Raspberry Pi along with Raspberry Pi camera. The obstacles are detected using a deep learning model built on MobileNetV2 SSD. The model is retrained using a dedicated dataset that was built for this purpose. Bounding boxes are used to mark detected obstacles; and feed them as features to the image space obstacle avoidance module. Figure 1 depicts internal view of what does the system see and an abstract description of our system's functionality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.