Computer and Robot Vision: Past, Present, and Future [TC Spotlight]

摘要

Robot perception is the capability of a robot to estimate and understand its surroundings to the degree that enables it to navigate and interact with the environment. Right at the core of robot perception lies the problem of building an internal model of the robot’s surroundings using onboard sensor data and prior knowledge. Although the internal model of the environment can be purely geometric (e.g., a point cloud)—as in traditional simultaneous localization and mapping (SLAM)—it can also contain higher-level structures, such as objects and other semantic elements of the scene (e.g., buildings, roads, pedestrians). In this sense, robot perception is related to the topic of scene understanding in the computer vision literature. Due to its crucial role in enabling robotics applications, ranging from navigation to manipulation and human–robot interaction, robot perception has been at the center stage of robotics research for more than 50 years <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref>. Similarly, perception has been a core topic in the computer vision community since its inception <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref>.