Qualcomm Incorporated

Nio Technology (anhui) Co., Ltd.

Nio Usa, Inc.

Tong Lin

Xu Chen

Hiu Hong Yu

Zhenxiang Jian

Dominic Gerard Farmer

Jie Wu

William J Morrison

Krishnaranjan Rao

Farzad Cyrus Foroughi Abari

Veera Ganesh Yalla

Zhongkui Wang

Waylon Y Chen

Andre Michelin

Antonio Antonellis Rufo

William James Morrison

Krishnaranjan S Rao

A calibration system for multi-sensor extrinsic calibration in a vehicle includes one or more calibration targets provided around an external environment within a threshold distance of the vehicle. Each of the one or more calibration targets includes a combination of sensor targets configured to be measured by and used for calibrating a pair of sensors selected from the group consisting of a first sensor, a second sensor or a third sensor. The system also includes a vehicle placement section configured to accommodate the vehicle on the vehicle placement section for detection of the one or more calibration targets.

High precision multi-sensor extrinsic calibration via production line and mobile station

Embodiments of the present disclosure are directed to providing scene perception display requiring reduced processing capabilities. Sensor data indicative of one or more targets from an imaging and ranging subsystem, location data from a positioning subsystem defining a geographical location of the imaging and ranging subsystem and orientation data from an orientation subsystem defining an orientation of the imaging and ranging subsystem are received. Doppler point cloud data is generated based on Doppler information and point cloud data from the sensor data, the location data and the orientation data. The targets are classified as either a static or dynamic target. Afterwards, the Doppler point cloud data is filtered by removing either the dynamic or the static targets from the Doppler point cloud data. The Doppler point cloud data of either the dynamic or the static targets are further processed and the further processed Doppler point cloud data is rendered.

Scene perception using coherent doppler LiDAR

Embodiments of the present disclosure are directed to calibrating an imaging and ranging subsystem. Sensor data indicative of one or more targets from the imaging and ranging subsystem, location data defining a geographical location of the imaging and ranging subsystem, orientation data defining an orientation of the imaging and ranging subsystem and stored translation and rotation values of the imaging and ranging subsystem are received. Estimated Doppler values for the target are provided by the sensor data and theoretical Doppler values for the targets are also calculated. The estimated Doppler values are compared to the theoretical Doppler values to determine if calibration of the imaging and ranging subsystem is required. If calibration is necessary, correction translation and correction rotation values are calculated in order to calculate updated translation and rotation values used to calibrate the imaging and ranging subsystem.

Real-time online calibration of coherent doppler lidar systems on vehicles

Extrinsic calibration of a Light Detection and Ranging (LiDAR) sensor and a camera can comprise constructing a first plurality of reconstructed calibration targets in a three-dimensional space based on physical calibration targets detected from input from the LiDAR and a second plurality of reconstructed calibration targets in the three-dimensional space based on physical calibration targets detected from input from the camera. Reconstructed calibration targets in the first and second plurality of reconstructed calibration targets can be matched and a six-degree of freedom rigid body transformation of the LiDAR and camera can be computed based on the matched reconstructed calibration targets. A projection of the LiDAR to the camera can be computed based on the computed six-degree of freedom rigid body transformation.

Automatic autonomous vehicle and robot LiDAR-camera extrinsic calibration

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.

Methods to improve location/localization accuracy in autonomous machines with GNSS, LIDAR, RADAR, camera, and visual sensors

Techniques are provided to quickly estimate a temporal shift of a GPS signal based on an analysis of a GLONASS signal. The shift can be a result of applied leap-second adjustments affecting GLONASS signals but not GPS signals. By identifying this shift, GPS and GLONASS signals can be considered together in order to estimate locations. The temporal shift can be determined, e.g., by estimating a separation between a GPS-signal frame feature (e.g., frame onset) and a GLONASS-signal frame feature (e.g., frame onset), or identifying coinciding frame features (e.g., a GPS-signal subframe coinciding with a GLONASS-signal string number). The techniques allow the temporal shift to be estimated based on an analysis of just a portion of the GPS-signal and GLONASS-signal frames, such that a speed of location estimations can be improved.

UTC time offset estimation at a GNSS receiver

Disclosed are methods, systems and/or devices to calibrate a network time by acquisition of satellite positioning system (SPS) signals and different instances of time, and time-tagging SPS times according to the network time. In particular, the network time may be calibrated based, at least in part, on a first difference between first and second SPS times obtained at two SPS position fixes and a second difference between corresponding first and second time stamps.

Methods and systems for mobile device clock management

Techniques for managing power consumption of a Global Navigation Satellite System (GNSS) receiver of a mobile device are provided. These techniques include a method that includes deriving a GNSS search window for the GNSS receiver based on a position uncertainty (PUNC) and a time uncertainty (TUNC), selecting a GNSS search mode based on the GNSS search window and resources available for searching for signals from GNSS satellite vehicles (SVs), wherein an estimated power consumption associated with execution of a GNSS search associated with the GNSS search mode does not exceed a power consumption limit specified for the GNSS receiver conducting the GNSS search using the GNSS search mode, and estimating a position of the mobile device.

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Cupertino, CA, United States of America

Tong Lin