Systems, methods, and computer-readable media are provided for wearable navigation systems. In some examples, a wearable navigation system may determine the distance between an object and one or more distance sensors based on signals received from an environment, where the distance sensors are part of a sensor package. In some aspects, the wearable navigation system may determine, using an inertial measurement unit, a position, speed, and acceleration of the wearable navigation system, where the inertial measurement unit is part of the sensor package. In some cases, the wearable navigation system may determine, by a Central Processing Unit (CPU) coupled to the sensor package, information associated with the object with respect to the wearable navigation system. In some instances, the wearable navigation system may generate, by an actuator system coupled to the sensor package, feedback signals in response to information associated with the object.

Systems and methods for updating navigational maps based using at least one sensor are provided. In one aspect, a control system for an autonomous vehicle, includes a processor and a computer-readable memory configured to cause the processor to: receive output from at least one sensor located on the autonomous vehicle indicative of a driving environment of the autonomous vehicle, retrieve a navigational map used for driving the autonomous vehicle, and detect one or more inconsistencies between the output of the at least one sensor and the navigational map. The computer-readable memory is further configured to cause the processor to: in response to detecting the one or more inconsistencies, trigger mapping of the driving environment based on the output of the at least one sensor, update the navigational map based on the mapped driving environment, and drive the autonomous vehicle using the updated navigational map.

A system to provide navigation solutions for vehicle landing guidance comprises onboard aiding sensors, an IMU, a radar altimeter, a map database, and a navigation system including a navigation filter that outputs estimated kinematic state statistics for the vehicle. An onboard processor inputs horizontal and vertical position statistics from the navigation filter into the map database, and computes an estimated ground/object height, ground/object velocity, ground/object acceleration, and error statistics thereof, based on terrain and object map data. The processer includes a radar altimeter inertial vertical loop (RIVL) filter that determines relative vertical acceleration based on a difference between vehicle vertical acceleration and ground/object vertical acceleration; determines relative vertical velocity based on a difference between vehicle vertical velocity and ground/object vertical velocity; performs consistency checks on the relative vertical acceleration and relative vertical velocity; and outputs estimated vehicle vertical position and vertical velocity statistics for compensation of the navigation filter outputs.

A box regularized particle filtering method implements a binary representation of numbers. This implementation can be used to determine a box division coordinate and/or to modify state intervals according to a fixed probability kernel, for example according to an Epanechnikov kernel. The method can be executed autonomously within a navigation system using measurement correlation, in particular on board an aircraft such as an airplane, a flying drone or any self-propelled airborne vehicle.

Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

A method may include obtaining sensor data about a total measurable world around an autonomous vehicle. The sensor data may be captured by sensor units co-located with the autonomous vehicle. The method may include generating a mapping dataset including the obtained sensor data and identifying data elements that each represents a point in the mapping dataset. The method may include sorting the data elements according to a structural data categorization that is a template for a high-definition map of the total measurable world and determining a mapping trajectory of the autonomous vehicle. The mapping trajectory may describe a localization and a path of motion of the autonomous vehicle. The method may include generating the high-definition map based on the structural data categorization and relative to the mapping trajectory of the autonomous vehicle, and the high-definition map may be updated based on the path of motion of the autonomous vehicle.

The purpose of the present disclosure is to set the initial information of the anchoring object (target) of a ship with high accuracy. A ship navigation assistance system according to the present disclosure includes a provisional initial information specifier, a measurement sensor and processing circuitry. The provisional initial information specifier may accept a specification of provisional initial information for characteristic information on an object to which a ship anchors or docks (docks to a pier). The measurement sensor may acquire measurement information on an object using a ranging result of an area including the object.

The purpose of the present disclosure is to suppress an error which occurs in movement, such as in anchoring a ship. A ship navigation assistance system includes a measurement sensor and a characteristic information updating module. The measurement sensor acquires measurement information on an object using a ranging result of an area including the object that is an anchorage target of a ship. The characteristic information updating module updates characteristic information on the object using initial characteristic information on the object or characteristic information before updating on the object, and the measurement information.

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

A driver assistance system for an ego vehicle, and a method for a driver assistance system is provided. The system is configured to refine a coarse geolocation method based on the detection of the static features located in the vicinity of the ego vehicle. The system performs at least one measurement of the visual appearance of each of at least one static feature located in the vicinity of the ego vehicle. Using the at least one measurement, a position of the ego vehicle relative to the static feature is calculated. The real world position of the static feature is identified. The position of the ego vehicle relative to the static feature is calculated, which is, in turn, used to calculate a static feature measurement of the vehicle location. The coarse geolocation measurement and the the static feature measurement are combined to form a fine geolocation position. By combining the measurements, a more accurate location of the ego vehicle can be determined.