A method including propelling a vehicle disposed in a medium. The vehicle includes a body, a propulsion mechanism connected to the body, and a direction control system. The vehicle is subject to advection caused by movement of the medium. The method also includes commanding the vehicle to perform a navigation course comprising a closed course-over-ground. The method also includes periodically adjusting navigation of the vehicle along the closed course-over-ground such that a course-through-the-medium turn-rate is varied in a manner that causes a course-over-ground turn-rate of the vehicle to be held constant, thereby minimizing the impact of medium advection on vehicle speed over ground.

In accordance with one implementation of the present disclosure, a new approach for determining a movement orientation of a user is proposed in indoor navigation. Generally speaking, a device orientation of a terminal device is obtained based on at least one signal stream collected from the terminal device carried by a moving user. A deviation degree is determined based on the at least one signal stream, here the deviation degree represents a deviation between a movement orientation of the user and an actual device orientation of the terminal device. The movement orientation is determined based on the device orientation in accordance with a determination that the deviation degree is below a threshold degree. With the above implementation, the movement orientation of the user is determined in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

In accordance with one implementation of the present disclosure, a new approach for identifying a stepping event is proposed in indoor navigation. Generally speaking, a first signal fragment and a second signal fragment respectively within a first time window and a second time window in an acceleration signal stream are obtained, here the acceleration signal stream is collected from an acceleration sensor associated with a moving user, the first time window being shorter than the second time window. A first amplitude feature and a second amplitude feature are determined for the first and second time windows based on the first and second signal fragments, respectively. A stepping event of the user is identified based on a deviation between the first and second amplitude features. With the above implementation, the stepping event is identified in a more effective an accurate way, and thus accuracy of the indoor navigation is increased.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

The present invention is a method and a device for tracking the unloading and loading of containers from and onto carrying platforms of tracks, using unloading and loading motion activity time course patterns characteristic of a specific container in combination with a specific carrying platform of a truck. In the invention the device is connected to a specific container and records the tilting angle of the container in the time course of unloading and/or loading from and onto a specific platform of the truck. A digital data processor produces from the data a motion activity time course pattern curve which is compared and matched for fitness to a predetermined standard motion activity time course pattern curve for the specific container and specific carrying platform of a truck and sends the degree of fitness to a data receiving terminal system. If the fitness of the two curves is found to be in close fitness, together with GPS data from the device, it is possible to determine if, where and when unloading and loading of the specific container took place.

A robotic system is disclosed that uses autonomous tunnel navigation. The system includes a plurality of sensors (e.g., ranging, odometry) to measure a distance from the robotic system to a plurality of walls. Memory stores instructions and a processor is coupled to the memory and the plurality of sensors to execute the instructions. The instructions cause the robotic system to detect movement of the robotic system through a surrounding environment based on sensor measurements, determine if the robotic system is in a tunnel based on the sensor measurements, and navigate with the odometry-based sensor when the robotic system is determined to be in the tunnel or the ranging sensor when the robotic system is determined to be not in the tunnel.

A robotic system is disclosed that uses autonomous tunnel navigation. The system includes a plurality of sensors (e.g., ranging, odometry) to measure a distance from the robotic system to a plurality of walls. Memory stores instructions and a processor is coupled to the memory and the plurality of sensors to execute the instructions. The instructions cause the robotic system to detect movement of the robotic system through a surrounding environment based on sensor measurements, determine if the robotic system is in a tunnel based on the sensor measurements, and navigate with the odometry-based sensor when the robotic system is determined to be in the tunnel or the ranging sensor when the robotic system is determined to be not in the tunnel.

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.