Semantic models for robot autonomy on dynamic sites

A method comprising:

• receiving, by data processing hardware of a robot, sensor data from one or more sensors of the robot, the sensor data indicating a first set of features;
• filtering, by the data processing hardware, the first set of features to obtain a filtered set of features, wherein filtering the first set of features is based on a comparison of the sensor data to semantic data indicating one or more objects;
• generating, by the data processing hardware, in a first map, one or more localization reference points corresponding to the filtered set of features;
• based on generating the one or more localization reference points in the first map, instructing, by the data processing hardware, determination of a location of the robot using the first map;
• and
• instructing, by the data processing hardware, performance of an action by the robot based on the location of the robot and using a second map generated based on the first set of features, wherein the first map and the second map correspond to different features.

A robot comprising:

• a body;
• two or more legs coupled to the body;
• one or more sensors coupled to the body;
• data processing hardware;
• and
• memory hardware in communication with the data processing hardware, the memory hardware storing instructions, wherein, based on execution of the instructions, the data processing hardware is configured to:
• receive sensor data from the one or more sensors, the sensor data indicating a first set of features;
• filter the first set of features to obtain a filtered set of features, wherein filtering the first set of features is based on a comparison of the sensor data to semantic data indicating one or more objects;
• generate, in a first map, one or more localization reference points corresponding to the filtered set of features;
• based on generating the one or more localization reference points in the first map, instruct determination of a location of the robot using the first map;
• and
• instruct performance of an action by the robot based on the location of the robot and using a second map generated based on the first set of features, wherein the first map and the second map correspond to different features.

A computing system comprising:

• data processing hardware;
• and
• memory hardware in communication with the data processing hardware, the memory hardware storing instructions, wherein, based on execution of the instructions, the data processing hardware is configured to:
• receive sensor data from one or more sensors of a robot, the sensor data indicating a first set of features;
• filter the first set of features to obtain a filtered set of features, wherein filtering the first set of features is based on a comparison of the sensor data to semantic data indicating one or more objects;
• generate, in a first map, one or more localization reference points corresponding to the filtered set of features;
• based on generating the one or more localization reference points in the first map, instruct determination of a location of the robot using the first map;
• and
• instruct performance of an action by the robot based on the location of the robot and using a second map generated based on the first set of features, wherein the first map and the second map correspond to different features.