物体の意味を考慮したステレオ視覚によるロボット障害物回避
Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
視覚インスタンスセグメンテーションと深度マップを組み合わせ、物体の種類に応じて回避の慎重さを変える障害物回避手法を提案し、地上・水中のシミュレーションで有効性を示した。
著者: Jungseok Hong, Karin de Langis, Cole Wyeth, Christopher Walaszek, Junaed Sattar
分類: cs.RO
原文アブストラクト
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.
