Gm Cruise Holdings LLC

Gm Global Technolgoy Operations, Inc.

University of Minnesota

Georgios Georgiou

Charles Bruce Matlack

Stergios I Roumeliotis

Chao X Guo

Joel Hesch

Juan Fasola

Clement Creusot

Esha D Nerurkar

Matthias Wisniowski

Kejian J Wu

Ryan C DuToit

Kourosh Sartipi

The subject disclosure relates to techniques for providing augmented reality (AR) navigation guidance to users of an autonomous vehicle (AV) ride hailing service. In some aspects, a method of the disclosed technology includes steps for transmitting an autonomous vehicle (AV) ride request to an AV dispatch service, receiving a ride confirmation indicating that an AV has been dispatched to a rider associated with the mobile device, detecting arrival of the AV at a pick-up location associated with the rider, and initializing augment reality (AR) guidance on the mobile device, wherein the AR guidance is configured to provide the rider with navigation information to facilitate pick-up by the AV. Systems and computer-readable media are also provided.

Map driven augmented reality

A vision-aided inertial navigation system (VINS) comprises an image source for producing image data along a trajectory. The VINS further comprises an inertial measurement unit (IMU) configured to produce IMU data indicative of motion of the VINS and an odometry unit configured to produce odometry data. The VINS further comprises a processor configured to compute, based on the image data, the IMU data, and the odometry data, state estimates for a position and orientation of the VINS for poses of the VINS along the trajectory. The processor maintains a state vector having states for a position and orientation of the VINS and positions within the environment for observed features for a sliding window of poses. The processor applies a sliding window filter to compute, based on the odometry data, constraints between the poses within the sliding window and compute, based on the constraints, the state estimates.

Vision-aided inertial navigation system for ground vehicle localization

The subject disclosure relates to techniques for providing augmented reality (AR) navigation assistance to users of an autonomous vehicle (AV) ride hailing service. In some aspects, a process for implementing the disclosed technology can include steps for navigating an autonomous vehicle (AV) along a route terminating in a drop-off location specified by a rider of the AV, detecting an arrival of the AV at the drop-off location, sending location information of the AV to a mobile device associated with the rider, and initializing augment reality (AR) guidance for the rider on the mobile device, wherein the AR guidance is configured to provide the rider with navigation information pertaining to the drop-off location. Systems and computer-readable media are also provided.

Systems, methods, and computer-readable media are provided for receiving traffic object data from a plurality of autonomous vehicles, comparing the traffic object data of each of the plurality of autonomous vehicles with known traffic object data, determining a discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, grouping the traffic object data of each of the plurality of autonomous vehicles based on the determining of the discrepancy between the traffic object data of each of the plurality of autonomous vehicles and the known traffic object data, determining whether a group of traffic object data of the grouping of the traffic object data of each of the plurality of autonomous vehicles exceeds a threshold, and updating a traffic object map based on the traffic object data of the group that exceeds the threshold.

Updating maps based on traffic object detection

Autonomous vehicles and techniques that can be utilized to compress point cloud data and operate on compressed point cloud data are provided. An autonomous vehicle can include a data compression system can configure point cloud data according to a collection of three-dimensional (3D) tiles representative of the region. Each 3D tile can include a portion of the cloud point data, where each point vector in the portion of the cloud point data can be configured relative to a position vector of the 3D tile defined in a coordinate system of the collection of 3D tiles. The data compression system can utilize a fixed-point Q-format representation based on a defined number of bits to compress at least a portion of the point cloud data. The autonomous vehicle also can include a control system that can operate mathematically on compressed point cloud data, without reliance on prior decompression.

Point cloud data compression in an autonomous vehicle

Systems and method are provided for localizing a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from a sensor of the vehicle; filtering, by the processor, the sensor data for data associated with static elements of the environment; determining, by the processor, realtime height values associated with the static elements; correlating, by the processor, the realtime height values with defined height values associated with a map of the environment; localizing, by the processor, the vehicle on the map based on the correlated realtime height values and the defined height values; and controlling, by the processor, the vehicle based on the localizing.

Methods and systems for localization of a vehicle

A method includes: receiving, with a computing platform, respective trajectory data and map data independently generated by each of a plurality of vision-aided inertial navigation devices (VINS devices) traversing an environment, wherein the trajectory data specifies poses along a path through the environment for the respective VINS device and the map data specifies positions of observed features within the environment as determined by an estimator executed by the respective VINS device; determining, with the computing platform and based on the respective trajectory data and map data from each of the VINS devices, estimates for relative poses within the environment by determining transformations that geometrically relate the trajectory data and the map data between one or more pairs of the VINS devices; and generating, with the computing platform and based on the transformations, a composite map specifying positions within the environment for the features observed by the VINS devices.

Growing community of inventors

San Francisco, CA, United States of America

Georgios Georgiou