Abstract: An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

Abstract: A system and method for estimating free space and assigning free space probabilities in point cloud data associated with an autonomous vehicle traveling on a surface, including taking into account sensor noise, sensor availability, obstacle heights, and distance of obstacles from the sensor. System and method can include determining surface planes and classifying point cloud points according to whether or not the points fall on surface planes, among other factors.

Abstract: A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.

Abstract: A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.

Abstract: A system and method for estimating free space and assigning free space probabilities in point cloud data associated with an autonomous vehicle traveling on a surface, including taking into account sensor noise, sensor availability, obstacle heights, and distance of obstacles from the sensor. System and method can include determining surface planes and classifying point cloud points according to whether or not the points fall on surface planes, among other factors.

Abstract: A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.

Abstract: A system for identifying desired information from collected sensor data includes a collection device and a processing module. The collection device collects sensor data, coarsely filters the sensor data according to predefined rules to generate filter matched data, and securely transmits the filter matched data to the processing module. The processing module finely filters the filter matched data to generate desired information, provides the desired information to an authorized actor, and deletes the filter matched data.

Abstract: A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.

Abstract: System and method for object detection. Images from cameras are provided to an inference engine to detect objects in real time, providing the images to an inference engine to detect the non-background and background pixels of the objects in the images, determining the position and size of the objects in the images based on contemporaneously gathered LiDAR data and the relationship of non-background to background pixels.

Abstract: An autonomous vehicle having sensors advantageously varied in capabilities, advantageously positioned, and advantageously impervious to environmental conditions. A system executing on the autonomous vehicle that can receive a map including, for example, substantially discontinuous surface features along with data from the sensors, create an occupancy grid based upon the map and the data, and change the configuration of the autonomous vehicle based upon the type of surface on which the autonomous vehicle navigates. The device can safely navigate surfaces and surface features, including traversing discontinuous surfaces and other obstacles.

Abstract: A system and method for estimating free space and assigning free space probabilities in point cloud data associated with an autonomous vehicle traveling on a surface, including taking into account sensor noise, sensor availability, obstacle heights, and distance of obstacles from the sensor. System and method can include determining surface planes and classifying point cloud points according to whether or not the points fall on surface planes, among other factors.

Abstract: A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.