Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
This work addresses mobile robots in unstructured environments by improving obstacle avoidance with semantic awareness, though it is incremental as it builds on existing avoidance techniques.
The paper tackled the problem of robotic obstacle avoidance by incorporating semantic object identities into navigation decisions, enabling differential responses based on object types like humans, which resulted in more efficient navigation strategies validated in simulated terrestrial and underwater environments.
Mobile robots in unstructured, mapless environments must rely on an obstacle avoidance module to navigate safely. The standard avoidance techniques estimate the locations of obstacles with respect to the robot but are unaware of the obstacles' identities. Consequently, the robot cannot take advantage of semantic information about obstacles when making decisions about how to navigate. We propose an obstacle avoidance module that combines visual instance segmentation with a depth map to classify and localize objects in the scene. The system avoids obstacles differentially, based on the identity of the objects: for example, the system is more cautious in response to unpredictable objects such as humans. The system can also navigate closer to harmless obstacles and ignore obstacles that pose no collision danger, enabling it to navigate more efficiently. We validate our approach in two simulated environments: one terrestrial and one underwater. Results indicate that our approach is feasible and can enable more efficient navigation strategies.