Abstract: Some techniques are described herein for providing different modes, each mode allowing for a different sensor to detect data. For example, a first mode can be configured to detect data via a touch sensor, a second mode can be configured to detect data via an infrared camera, and a third mode can be configured to detect data via an optical camera. In such an example, different modes can be configured to detect data using a different set of sensors that are in communication with a computer system.

Abstract: Some techniques are described herein for initiating a secure communication between a mount and a computer system. Such techniques include the mount detecting that the computer system is coupled to the mount and, in response, sending a message to the computer system with an identifier of the mount. The mount then receives a request to establish the secure communication with the computer system via a different communication channel than used to send the message. For example, the message can be sent using Near Field Communication (NFC) and the request can be to establish a short-range communication channel using Bluetooth. Other techniques are described herein for reconnecting a secure communication between a mount and a computer system using similar techniques as described above.

Abstract: A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

Abstract: According to one aspect, a system for robust localization may include a scan accumulator, a scan matcher, a transform maintainer, and a location fuser. The scan accumulator may receive a set of sensor data from a set of sensors mounted on a vehicle. The scan accumulator may generate a sensor scan point cloud output by transforming the set of sensor data from each sensor frame to a corresponding vehicle frame and calculate a fitness score, a transformation probability, and a mean elevation angle used to determine a scan confidence for the sensor data. The transform maintainer may receive GPS data, the scan confidence, and the matched sensor scan point cloud output and map tile point cloud data from the scan matcher, and determine whether the GPS data or the matched sensor scan point cloud output and map tile point cloud data is utilized for a map-to-odometer transformation output.

Abstract: The present disclosure generally relates to methods and systems for determining a position of a vehicle relative to a surrounding environment. A vehicle may obtain a LiDAR map of the surrounding environment at a vehicle location via processing data from a LiDAR device mounted on the vehicle. The vehicle may access a transition map based on the vehicle location to determine a transformation between the LiDAR map and a stored high-definition map. The transition map may define a six degrees of freedom transformation for each of a plurality of locations. The vehicle may apply the transformation to an element of the LiDAR map to determine a corresponding location of the element on the high-definition map. In some aspects, the vehicle may perform an autonomous driving operation based on a location of the vehicle with respect to the element of the high-definition map.

Abstract: According to one aspect, a system for robust localization may include a scan accumulator, a scan matcher, a transform maintainer, and a location fuser. The scan accumulator may receive a set of sensor data from a set of sensors mounted on a vehicle. The scan accumulator may generate a sensor scan point cloud output by transforming the set of sensor data from each sensor frame to a corresponding vehicle frame and calculate a fitness score, a transformation probability, and a mean elevation angle used to determine a scan confidence for the sensor data. The transform maintainer may receive GPS data, the scan confidence, and the matched sensor scan point cloud output and map tile point cloud data from the scan matcher, and determine whether the GPS data or the matched sensor scan point cloud output and map tile point cloud data is utilized for a map-to-odometer transformation output.

Abstract: A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

Abstract: The present disclosure generally relates to methods and systems for determining a position of a vehicle relative to a surrounding environment. A vehicle may obtain a LiDAR map of the surrounding environment at a vehicle location via processing data from a LiDAR device mounted on the vehicle. The vehicle may access a transition map based on the vehicle location to determine a transformation between the LiDAR map and a stored high-definition map. The transition map may define a six degrees of freedom transformation for each of a plurality of locations. The vehicle may apply the transformation to an element of the LiDAR map to determine a corresponding location of the element on the high-definition map. In some aspects, the vehicle may perform an autonomous driving operation based on a location of the vehicle with respect to the element of the high-definition map.