  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.
