Abstract: Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a method includes receiving image data about an object and receiving depth data about the object. The method also includes generating a visual estimate of the object based on the image data and the depth data. The method further includes receiving tactile data about the object. The method yet further includes generating a tactile estimate of the object based on the tactile data. The method includes estimating a pose of the object based on the visual estimate and the tactile estimate.

Abstract: An autonomous vehicle capable of trajectory prediction may include a first sensor, a second sensor, a processor, a trajectory planner, a low-level controller, and vehicle actuators. The first sensor may be of a first sensor type and may detect an obstacle and a goal. The second sensor may be of a second sensor type and may detect the obstacle and the goal. The processor may perform matching on the obstacle detected by the first sensor and the obstacle detected by the second sensor, model an existence probability of the obstacle based on the matching, and track the obstacle based on the existence probability and a constant velocity model. The trajectory planner may generate a trajectory for the autonomous vehicle based on the tracked obstacle, the goal, and a non-linear model predictive control (NMPC). The low-level controller may implement the trajectory for the autonomous vehicle by driving vehicle actuators.

Abstract: A system and method for providing a comprehensive trajectory planner for a person-following vehicle that includes receiving image data and LiDAR data associated with a surrounding environment of a vehicle. The system and method also include analyzing the image data and detecting the person to be followed that is within an image and analyzing the LiDAR data and detecting an obstacle that is located within a predetermined distance from the vehicle. The system and method further include executing a trajectory planning algorithm based on fused data associated with the detected person and the detected obstacle.

Abstract: Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a method includes receiving image data about an object and receiving depth data about the object. The method also includes generating a visual estimate of the object based on the image data and the depth data. The method further includes receiving tactile data about the object. The method yet further includes generating a tactile estimate of the object based on the tactile data. The method includes estimating a pose of the object based on the visual estimate and the tactile estimate.

Abstract: A system and method for providing accurate trajectory following for automated vehicles in dynamic environments that include receiving image data and LiDAR data associated with a dynamic environment of a vehicle. The system and method also include processing a planned trajectory of the vehicle that is based on an analysis of the image data and LiDAR data. The system and method further include communicating control signals associated with following the planned trajectory to autonomously control the vehicle to follow the planned trajectory to navigate within the dynamic environment to reach a goal.

Abstract: An autonomous vehicle capable of trajectory prediction may include a first sensor, a second sensor, a processor, a trajectory planner, a low-level controller, and vehicle actuators. The first sensor may be of a first sensor type and may detect an obstacle and a goal. The second sensor may be of a second sensor type and may detect the obstacle and the goal. The processor may perform matching on the obstacle detected by the first sensor and the obstacle detected by the second sensor, model an existence probability of the obstacle based on the matching, and track the obstacle based on the existence probability and a constant velocity model. The trajectory planner may generate a trajectory for the autonomous vehicle based on the tracked obstacle, the goal, and a non-linear model predictive control (NMPC). The low-level controller may implement the trajectory for the autonomous vehicle by driving vehicle actuators.

Abstract: A system and method for providing a comprehensive trajectory planner for a person-following vehicle that includes receiving image data and LiDAR data associated with a surrounding environment of a vehicle. The system and method also include analyzing the image data and detecting the person to be followed that is within an image and analyzing the LiDAR data and detecting an obstacle that is located within a predetermined distance from the vehicle. The system and method further include executing a trajectory planning algorithm based on fused data associated with the detected person and the detected obstacle.