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Vision-based Obstacle Avoidance

We have developed efficient approaches to obstacle avoidance for humanoid robots based on monocular images. Our approach relies on ground-plane estimation and trains visual classifiers using color and texture information in a self-supervised way. During navigation, the classifiers are automatically updated and applied to the image stream to decide which areas are traversable. From this information, the robot can compute a two-dimensional occupancy grid map of the environment and use it for planning collision-free paths. As we illustrate in thorough experiments with a real humanoid, the classification results are highly accurate and the resulting occupancy map enables the robot to reliably avoid obstacles during navigation.


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Videos:

The videos below show how our Nao humanoid trains the visual classifiers in a self-supervised fashion during navigation. The learned classifiers are applied to the stream of camera images to distriminate obstacles from the floor. Based on the traversable area, the robot builds an occupancy map for collision-free navigaton.

In the first video, the robot uses data from its 2D laser scanner to guide the training, in the second video the robot needs only its RGB camera data and odometry information.

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