Abstract: The technology disclosed includes systems and methods for using a deep learning trained classifier to detect obstacles and pathways in an environment in which a robot moves. The system includes logic to receive image information captured by at least one visual spectrum-capable camera and location information captured by at least one depth measuring camera located on a mobile platform. The system includes logic to determine a three-dimensional 3D point cloud of points having 3D information. The system can determine, using an ensemble of trained neural network classifiers, an identity for objects. The system includes logic to determine an occupancy map of the environment. The system includes logic to provide the occupancy map to a process for initiating robot movement to avoid objects in the occupancy map of the environment.

Abstract: The technology disclosed includes systems and methods for preparing a segmented occupancy grid map based upon image information of an environment in which a robot moves. The image information is captured by at least one visual spectrum-capable camera and at least one depth measuring camera. The system includes logic to receive image information captured by at least one visual spectrum-capable camera and location information captured by at least one depth measuring camera located on a mobile platform. The system includes logic to extract from the image information, features in the environment. The system includes logic to determine a 3D point cloud of points having 3D information. The system includes logic to determine, from the 3D point cloud, an occupancy map of the environment. The system includes logic to segment the occupancy map into a segmented occupancy map of regions that represent rooms and corridors in the environment.