Abstract: A method of visual navigation for a robot includes integrating a depth map with localization information to generate a three-dimensional (3D) map. The method also includes motion planning based on the 3D map, the localization information, and/or a user input. The motion planning overrides the user input when a trajectory and/or a velocity, received via the user input, is predicted to cause a collision.

Abstract: A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

Abstract: Aspects of the disclosure are related to a method, apparatus and system for joint motion planning and trajectory estimation, comprising: determining a cost function to describe system kinematics comprising trajectories, speeds, and accelerations of a host vehicle and of one or more other vehicles for each possible intention of the host vehicle and of the other vehicles, wherein the trajectories are described with spline functions; and determining jointly the trajectories of the host vehicle and of the other vehicles.

Abstract: A method for guiding a robot equipped with a camera to facilitate three-dimensional (3D) reconstruction through sampling based planning includes recognizing and localizing an object in a two-dimensional (2D) image. The method also includes computing 3D depth maps for the localized object. A 3D object map is constructed from the depth maps. A sampling based structure is grown around the 3D object map and a cost is assigned to each edge of the sampling based structure. The sampling based structure may be searched to determine a lowest cost sequence of edges that may, in turn be used to guide the robot.

Abstract: Aspects of the disclosure are related to a method, apparatus, and system for planning a motion for a first vehicle, comprising: estimating past states of an observed second vehicle based on sensor inputs; predicting a future trajectory of the second vehicle based on the estimated past states; planning a future trajectory of the first vehicle based on the predicted future trajectory of the second vehicle and a safety cost function; and driving the first vehicle to follow the planned trajectory.

Abstract: A method of motion planning includes observing an object from a first pose of an agent having a controllable camera. The method also includes determining one or more subsequent control inputs to move the agent and the camera to observe the object from at least one subsequent pose. The subsequent control input(s) are determined so as to minimize an expected enclosing measure of the object based on visual data collected from the camera. The method further includes controlling the agent and the camera based on the subsequent control input(s).

Abstract: Aspects of the disclosure are related to a method, apparatus and system for joint motion planning and trajectory estimation, comprising: determining a cost function to describe system kinematics comprising trajectories, speeds, and accelerations of a host vehicle and of one or more other vehicles for each possible intention of the host vehicle and of the other vehicles, wherein the trajectories are described with spline functions; and determining jointly the trajectories of the host vehicle and of the other vehicles.

Abstract: A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

Abstract: A method for mapping an environment by an electronic device is described. The method includes obtaining a set of sensor measurements. The method also includes determining a set of voxel occupancy probability distributions respectively corresponding to a set of voxels based on the set of sensor measurements. Each of the voxel occupancy probability distributions represents a probability of occupancy of a voxel over a range of occupation densities. The range includes partial occupation densities.

Abstract: Aspects of the disclosure are related to a method, apparatus, and system for planning a motion for a first vehicle, comprising: estimating past states of an observed second vehicle based on sensor inputs; predicting a future trajectory of the second vehicle based on the estimated past states; planning a future trajectory of the first vehicle based on the predicted future trajectory of the second vehicle and a safety cost function; and driving the first vehicle to follow the planned trajectory.

Abstract: A method for mapping an environment by an electronic device is described. The method includes obtaining a set of sensor measurements. The method also includes determining a set of voxel occupancy probability distributions respectively corresponding to a set of voxels based on the set of sensor measurements. Each of the voxel occupancy probability distributions represents a probability of occupancy of a voxel over a range of occupation densities. The range includes partial occupation densities.

Abstract: A method for guiding a robot equipped with a camera to facilitate three-dimensional (3D) reconstruction through sampling based planning includes recognizing and localizing an object in a two-dimensional (2D) image. The method also includes computing 3D depth maps for the localized object. A 3D object map is constructed from the depth maps. A sampling based structure is grown around the 3D object map and a cost is assigned to each edge of the sampling based structure. The sampling based structure may be searched to determine a lowest cost sequence of edges that may, in turn be used to guide the robot.

Abstract: A method of visual navigation for a robot includes integrating a depth map with localization information to generate a three-dimensional (3D) map. The method also includes motion planning based on the 3D map, the localization information, and/or a user input. The motion planning overrides the user input when a trajectory and/or a velocity, received via the user input, is predicted to cause a collision.

Abstract: A method of motion planning for an agent to reach a target includes determining a frontier region between a frontier at a current time and a frontier at a next time. Waypoints are sampled in the frontier region with a bias toward the target. A path to reach the target is selected based on a sequence of the sampled waypoints.

Abstract: A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

Abstract: A method of motion planning includes observing an object from a first pose of an agent having a controllable camera. The method also includes determining one or more subsequent control inputs to move the agent and the camera to observe the object from at least one subsequent pose. The subsequent control input(s) are determined so as to minimize an expected enclosing measure of the object based on visual data collected from the camera. The method further includes controlling the agent and the camera based on the subsequent control input(s).

Abstract: A method for generating a map includes determining an occupancy level of each of multiple voxels. The method also includes determining a probability distribution function (PDF) of the occupancy level of each voxel. The method further includes performing an incremental Bayesian update on the PDF to generate the map based on a measurement performed after determining the PDF.

Abstract: A method of calculating a most likely map based on batch data includes gathering a corpus of sensor measurements indexed by a location of a sensor throughout an environment to be mapped. The method also includes determining, after gathering the corpus of sensor measurements, a most likely occupancy level of each voxel of multiple voxels of the environment in accordance with the corpus of sensor measurements and a stochastic sensor model. The method further includes calculating the most likely map based on the determined most likely occupancy level.

Abstract: A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

Abstract: A method for generating a movement policy includes determining a probability distribution function for multiples nodes of a roadmap and determining, in parallel, a cost and a collision probability for each edge of the roadmap. The method also includes generating the movement policy based on the probability distribution function, the cost, and the collision probability.