Abstract:
An example system includes one or more laser sensors on a robotic device, where the one or more laser sensors are configured to produce laser sensor data indicative of a first area within a first distance in front of the robotic device. The system further includes one or more stereo sensors on the robotic device, where the stereo sensors on the robotic device are configured to produce stereo sensor data indicative of a second area past a second distance in front of the robotic device. The system also includes a controller configured to receive the laser sensor data, receive the stereo sensor data, detect one or more objects in front of the robotic device based on at least one of the laser sensor data and the stereo sensor data, and provide instructions for the robotic device to navigate based on the one or more detected objects.
Abstract:
Example systems and methods are disclosed for implementing vehicle operation limits to prevent vehicle load failure during vehicle teleoperation. The method may include receiving sensor data from sensors on a vehicle that carries a load. The vehicle may be controlled by a remote control system. The load weight and dimensions may be determined based on the sensor data. In order to prevent a vehicle load failure, a forward velocity limit and an angular velocity limit may be calculated. Vehicle load failures may include the vehicle tipping over, the load tipping over, the load sliding off of the vehicle, or collisions. The vehicle carrying the load may be restricted from exceeding the forward velocity limit and/or the angular velocity limit during vehicle operation. The remote control system may display a user interface indicating to a remote operator the forward velocity limit and the angular velocity limit.
Abstract:
Systems and methods are provided for generating maps with semantic labels. A computing device can determine a first map that includes features located at first positions and semantic labels located at semantic positions, and determine a second map that includes at least some of the features located at second positions. The computing device can identify a first region with fixed features located at first positions and corresponding equivalent second positions. The computing device can identify a second region with moved features located at first positions and corresponding non-equivalent second positions. The computing device can determine one or more transformations between first positions and second positions. The computing device can assign the semantic labels to the second map at second semantic positions, where the second semantic positions are the same in the first region, and where the second semantic positions in the second region are based on the transformation(s).