Abstract: Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

Abstract: Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

Abstract: Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

Abstract: Enclosed are embodiments of an ML-based framework for drivable surface annotation. In an embodiment, a method comprises: obtaining, using at least one processor, multimodal map data for a geographic region; and automatically annotating, using the at least one processor, one or more semantic masks of the map data using a machine learning model.

Abstract: Enclosed are embodiments of an ML-based framework for drivable surface annotation. In an embodiment, a method comprises: obtaining, using at least one processor, multimodal map data for a geographic region; and automatically annotating, using the at least one processor, one or more semantic masks of the map data using a machine learning model.

Abstract: Among other things, we describe techniques for generation of dimensionally reduced maps and localization in an environment for navigation of vehicles. The techniques include generating, using one or more sensors of a vehicle located at a spatiotemporal location within an environment, M-dimensional sensor data representing the environment, wherein M is greater than 2. Odometry data is generated representing an operational state of the vehicle. The odometry data is associated with the spatiotemporal location. An N-dimensional map is generated of the environment from the M-dimensional sensor data, wherein N is less than M. The generating of the N-dimensional map comprises extracting an M-dimensional environmental feature of the spatiotemporal location from the M-dimensional sensor data. The M-dimensional environmental feature is associated with the odometry data. An N-dimensional version of the M-dimensional environmental feature is generated.