An electronic landing platform state module is configured to generate a state estimation of a platform surface at sea includes a plurality of electronic platform state process modules configured to receive an output from a respective spectral sensor. The plurality of electronic platform state process modules are further configured to output a monitored spectral platform state signal in response to applying a spectral process on a respective output. Each spectral process corresponds to a particular spectral modality of the respective spectral sensor. The electronic landing platform state module further includes an electronic platform state estimator module configured to determine a corrected dynamic state of the platform in response to fusing together the individual monitored spectral platform state signals.

Systems and methods of path-based mapping and routing are provided. Translation information and absolute information of mobile objects in environments are determined based on a fusion of sensing data from a radar and an inertial measurement unit (IMU) including a gyroscope and an accelerometer, from which path-based maps and optimal routes can be generated.

An information processing apparatus performs first correction for correcting, in a case where a loop of a movement path of a sensor is detected, a position and orientation associated with a first measurement point used for estimation of a position and orientation to a position and orientation based on a second measurement point that is present near the sensor when the loop is detected.

In one embodiment, a system includes an inertial measurement unit, a lidar sensor, and one or more processors configured to perform operations. The operations include receiving data from the lidar sensor. The operations include determining movement data based on the data received from the lidar sensor. The operations include receiving data from the inertial measurement unit. The operations include determining, based on the movement data and the data received from the inertial measurement unit, one or more calibration factors for the inertial measurement unit. The operations include applying the one or more calibration factors to a measurement received from the inertial measurement unit.

The method for autonomous vehicle control preferably includes sampling measurements, determining refined sensor poses based on the measurements, determining an updated vehicle pose based on the measurements and operation matrix, optionally determining evaluation sensor poses based on the refined sensor poses, optionally updating the operation matrix(es) based on the evaluation sensor poses, and/or any other suitable elements. The system for autonomous vehicle control can include one or more vehicles, one or more sensors, one or more processing systems, and/or any other suitable components.

If a controller determines the calculation of a measured vehicle body velocity is possible, the controller calculates the measured vehicle body velocity as an estimated vehicle body velocity to conduct update processing for a K table and an AB table. If the controller determines the calculation of the measured vehicle body velocity is impossible, the controller extracts a conversion coefficient from the K table based on a detected running state while extracting a sensitivity coefficient and an offset coefficient from the AB table based on the temperature by a temperature sensor. The vehicle mounted apparatus calculates an axle pulse-based vehicle body velocity from the extracted conversion coefficient while calculating an acceleration-based vehicle body velocity from the extracted sensitivity coefficient and offset coefficient. The vehicle mounted apparatus calculates the estimated vehicle body velocity by weighting the calculation values of the axle pulse-based vehicle body velocity and the acceleration-based vehicle body velocity.

Disclosed is a method for resetting the estimated position of a vehicle, including: —a step of receiving by a RADAR system a real RADAR image, —a step of acquiring an estimated position of the vehicle, —a step of calculating by a computer equipping the vehicle a simulated RADAR image, as a function of the estimated position of the vehicle and of a cartographic model of the environment of the vehicle, —a step of comparing the real RADAR image and the simulated RADAR image, and —a step of correcting the estimated position of the vehicle as a function of the result of the comparison.

Autonomous merit-based heading alignment and initialization methods for inertial navigation systems that update heading alignment in parallel with a heading estimation process. In some embodiments, such methods calculate figures of merit (FOMs) for multiple heading observations and estimate heading based on the calculate FOMs. In some embodiments, sensor data are genericized and published to a generic network, and multiple heading channels listen to the generic network for the published sensor data to make the heading estimation process sensor-agnostic. Such methods can be performed by software and/or incorporated into hardware and/or software based autonomous merit-based inertial navigation systems, which in turn can be deployed in vehicles to effect autonomous navigation solutions.

Methods and systems include localization of a vehicle localize precisely and in near real-time. As described, localization of a vehicle using a Global Navigation Satellite System (GNSS) can comprise receiving a signal from each of a plurality of satellites of a GNSS constellation and receiving input from one or more sensors of the vehicle. The input from the sensors can indicate current physical surroundings of the vehicle. A model of the current physical surrounding of the vehicle can be generated based on the input from the one or more sensors of the vehicle. One or more multipath signals received from the plurality of satellites can be mitigated based on the model and the vehicle can be localized using the received signals from the plurality of satellites of the GNSS constellation and based on the mitigation of the one or more multipath signals.

A distributed centralized automatic driving method comprises a sensor processing module, a perception positioning module, a perception target detection module, a decision-making module, a planning module, and a vehicle control module. By clearly defining data domains and a control process, a modular design is performed to implement each functional method, and each module can be deployed to a control unit of the corresponding data domain according to the load of a computing platform. The distributed centralized automatic driving method has different computing requirements for different scenarios. By means of a distributed design, centralized computing is distributed to different computing unit modules, so as to greatly improve the stability, efficiency and parallelism of the method, thereby improving the overall performance of the method.