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.

Methods for radar altimeter integrity monitoring are provided. One method comprises obtaining one or more GNSS measurements, or one or more hybridized GNSS/INS measurements, in an earth-centered-earth-fixed (ECEF) coordinate frame for a vehicle; obtaining one or more altitude measurements from one or more radar altimeters; transforming the altitude measurements into the ECEF coordinate frame using a terrain map and a GNSS or hybridized GNSS/INS based position estimate with ensured integrity; determining a full solution estimate of position for the vehicle based on the transformed altitude measurements, and the GNSS or hybridized GNSS/INS measurements; determining one or more sub-solution estimates of position based on a subset of the transformed altitude measurements, and the GNSS or hybridized GNSS/INS measurements; comparing the full solution estimate with the sub-solution estimates using statistical analysis; and determining an altitude protection level based on a probability of hazardous misleading information and a probability of false detection.

Rate of penetration (ROP) measurement system (10) has sensor apparatus on a drill rig detecting drilling advancement. Sender (38, 200) transmits to a receiver (40, 204), optionally via a reflector (39, 208). An electronic sub (201) can include the sender (200), receiver (204) or reflector (208). Reflector (39, 208) reflects signals to the receiver (40, 204). Distance measurement or space mapping can use LIDAR/laser and MEMS mirror. Releasable attachment to the drill rig can be by magnet (112). Atmospheric or barometric pressure can be detected and pressure change can be used to determine distance moved. WOB, RPM, torque and time rate of progress can be measured and combined with distance moved measurements to assess wear on a drill bit. Near real time

ROP measurement can be calculated and displayed (17) and/or reported (21). Drilling efficiency and premature drill wear or change in rock can be determined.

Provided is a vision sensing device including a housing, a camera, a laser pattern generator, an inertial measurement unit, and at least one processor configured to project a laser pattern within the field of view of the camera, capture inertial data from the inertial measurement unit as a user moves the housing, capture visual data from the field of view with the camera as the user moves the housing, capture depth data with the laser pattern generator as the user moves the housing, and generate an RGB-D point cloud based on the visual data, the inertial data, and the depth data.

A vehicle, for example, an autonomous vehicle receives signals from a global navigation satellite system (GNSS) and determines accurate location of the vehicle using the GNSS signal. The vehicle performs localization to determine the location of the vehicle as it drives. The autonomous vehicle uses sensor data and a high definition map to determine an accurate location of the autonomous vehicle. The autonomous vehicle uses accurate location of the vehicle to determine RTK corrections that is used for improving GNSS location estimates at a future location. The RTK corrections may be transmitted to other vehicles.

A method for relocating a mobile vehicle in a simultaneous localization and mapping (SLAM) map is provided. The method can be used in the mobile vehicle moving in an area and includes: using SLAM to establish the SLAM map that corresponds to the area at an initial time point; detecting, by a non-SLAM positioning device, a first position trajectory and a first azimuth trajectory of the mobile vehicle on the SLAM map; detecting, by a SLAM positioning device, a loss probability of the mobile vehicle between a first timestamp and a second timestamp; determining whether a condition is satisfied; and updating the SLAM map to a new SLAM map corresponding to a current time point and updating positioning information of the mobile vehicle in the new SLAM map when the condition is satisfied at the current time point.

Provided is a process executed by a robot, including: traversing, to a first position, a first distance in a backward direction; after traversing the first distance, rotating in a first rotation; after the first rotation, traversing, to a second position, a second distance in a third direction; after traversing the second distance, rotating 180 degrees in a second rotation such that the field of view of the sensor points in a fourth direction; after the second rotation, traversing, to a third position, a third distance in the fourth direction; after traversing the second distance, rotating 180 degrees in a third rotation such that the field of view of the sensor points in the third direction; and after the third rotation, traversing, to a fourth position, a fourth distance in the third direction.

A system and method for multi-image-based vessel proximity situation recognition support is proposed. The system may include an unmanned surface vehicle (USV) configured to detect and track surrounding objects by monitoring surroundings using surrounding images and navigation sensors. The system may also include a remote navigation controller configured to support proximity situation recognition of the unmanned surface vehicle according to detection of the surrounding objects, wherein the unmanned surface vehicle may include an image acquisition processor, a navigation sensor, and a detector.

Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.

This presentation provides methods to track pedestrians heading angle using smart phone data. Tracking heading angle especially at or from stationary position is key for pedestrian safety, e.g., for smart cross system and pedestrian collision mitigation system. It provides pedestrian-to-vehicle (P2V) platform. It deploys smart phones or mobile devices, equipped with DSRC (Dedicated short range communication) support, to act as beacons for pedestrians: Phone can alert driver to pedestrian presence in path; Pedestrian Basic Safety Message (BSM) can aid awareness for vehicles; It can be used for bicycles, as well. It also provides pedestrian-to-infrastructure (P2I) platform. Smart phone, through DSRC/cellular, transmits pedestrian presence to crosswalks/signals: It enables advanced crosswalk lighting/warning scheme; It enables bundling of pedestrian presence to vehicles. In this presentation, we provide various examples and variations on these.