A planar-beam, light detection and ranging (PLADAR) system can include a laser scanner that emits a planar-beam, and a detector array that detects reflected light from the planar beam.

The present invention relates to an automated guided vehicle for transporting and placing a load, comprising a primary environmental sensor and at least one secondary environmental sensor, wherein the transport vehicle is configured, first, using the primary environmental sensor, to detect a drop-off location for the load and the region between the drop-off location and the transport vehicle and to check said drop-off location and said region for obstacles; if no obstacle is recognized, to travel to the drop-off location; during the journey to the drop-off location, to check the route and the drop-off location for obstacles using the secondary environmental sensor.

Techniques for multi-modal contextualization of object detection for use with an agricultural vehicle are described herein. The techniques can provide additional context information to radar detection to assess the likelihood of an object detected by radar of being an object of interest. Examples of detection that might be detected by a radar sensor that, based on the additional context information, the agricultural vehicle can make a determination to ignore can include ground targets, ghost targets, side or overhead reflections from obstacles, detections from tall crops/weeds in a field.

An autonomous truck loading system can have a database that stores a plurality of elementary behaviors for various phases of a process of loading the autonomous truck by the one or more loaders. The stored elementary behaviors can include predetermined maneuvers for the autonomous truck, sensing behaviors; and logic behaviors. An operator can select multiple ones of the stored behaviors via user interface. A controller can assemble the selected behaviors together into an operation script for loading of the autonomous truck by a loader. The controller can control the autonomous truck to perform the operation script.

Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a navigational system for a host vehicle may include at least one processor programmed to: receive a stream of images captured by a camera onboard the host vehicle, wherein the captured images are representative of an environment surrounding the host vehicle; and receive an output of a LIDAR onboard the host vehicle, wherein the output of the LIDAR is representative of a plurality of laser reflections from at least a portion of the environment surrounding the host vehicle. The at least one processor may also be configured to determine at least one indicator of relative alignment between the output of the LIDAR and at least one image captured by the camera; attribute LIDAR reflection information to one or more objects identified in the at least one image based on the at least one indicator of the relative alignment between the output of the LIDAR and the at least one image captured by the camera; and use the attributed LIDAR reflection information and the one or more objects identified in the at least one image to determine at least one navigational characteristic associated with the host vehicle.

A mobile delivery robot has at least one memory component containing at least map data; at least two cameras adapted to take visual images; and at least one processing component. The at least one processing component is adapted to at least extract straight lines from the visual images taken by the at least two cameras and compare them to the map data to at least localize the robot. The mobile robot employs a localization method which involves taking visual images with at least two cameras; extracting straight lines from the individual visual images with at least one processing component; comparing the extracted features with existing map data; and outputting a location hypothesis based on said comparison.

A mobile robot is configured to navigate on a sidewalk and deliver a delivery to a predetermined location. The robot has a body and an enclosed space within the body for storing the delivery during transit. At least two cameras are mounted on the robot body and are adapted to take visual images of an operating area. A processing component is adapted to extract straight lines from the visual images taken by the cameras and generate map data based at least partially on the images. A communication component is adapted to send and receive image and/or map data. A mapping system includes at least two such mobile robots, with the communication component of each robot adapted to send and receive image data and/or map data to the other robot. A method involves operating such a mobile robot in an area of interest in which deliveries are to be made.

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.

An example method includes receiving, from one or more sensors associated with an autonomous vehicle, sensor data associated with a target object in an environment of the vehicle during a first environmental condition, where at least one sensor of the sensor(s) is configurable to be associated with one of a plurality of operating field of view volumes. The method also includes based on the sensor data, determining at least one parameter associated with the target object. The method also includes determining a degradation in the parameter(s) between the sensor data and past sensor data, where the past sensor data is associated with the target object in the environment during a second environmental condition different from the first and, based on the degradation, adjusting the operating field of view volume of the at least one sensor to a different one of the operating field of view volumes.

Various embodiments include methods for improving navigation by a processor of a robotic device equipped with an image sensor and an optical flow sensor. The robotic device may be configured to capture or receive two image frames from the image sensor, generate a homograph computation based on the image frames, receive optical flow sensor data from an optical flow sensor, and determine a scale estimation value based on the homograph computation and the optical flow sensor data. The robotic device may determine the robotic device pose (or the pose of the image sensor) based on the scale estimation value.