Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
Semantically-Aware Strategies for Stereo-Visual Robotic Obstacle Avoidance
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DOI：
10.1109/icra48506.2021.9561863
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Jungseok Hong;Karin de Langis;Cole Wyeth;Christopher Walaszek;Junaed Sattar
中科院分区：
文献类型：
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作者：
Jungseok Hong;Karin de Langis;Cole Wyeth;Christopher Walaszek;Junaed Sattar
在非结构化、无地图环境中的移动机器人必须依靠避障模块来安全导航。标准回避技术估计障碍物相对于机器人的位置，但不知道障碍物的身份。因此，机器人在做出如何导航的决策时无法利用有关障碍物的语义信息。我们提出了一种避障模块，它将视觉实例分割与深度图相结合，以对场景中的对象进行分类和定位。系统根据对象的身份有差别地避开障碍物：例如，系统对人类等不可预测的对象做出更加谨慎的反应。该系统还可以导航到更接近无害的障碍物并忽略不构成碰撞危险的障碍物，从而使其能够更有效地导航。我们在两种模拟环境中验证了我们的方法：一种是陆地环境，一种是水下环境。结果表明我们的方法是可行的并且可以实现更有效的导航策略。
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.