Ford Global Technolgoies, LLC

Ganesh Kumar

Praveen Narayanan

Jinesh J Jain

Punarjay Chakravarty

Gaurav Pandey

Nikita Jaipuria

Brian George Buss

Apurbaa Mallik

Ramchandra Ganesh Karandikar

Md Nahid Pervez

Steven Chao

Syed Ahmed

Michael Feller

Sharnam Shah

A computer includes a processor and a memory storing instructions executable by the processor to determine an estimated pose of a vehicle in a global reference frame having a first error and to determine an estimated second error based on a combination of the first error and a map error, in which the map error represents a difference between the estimated pose of the vehicle in the global reference frame and a corresponding estimated pose in a map-referenced frame. The stored instructions being additionally to predict a third error in a future map-referenced measurement frame based on the estimated second error and a motion model and to compute an update to the third error by combining the predicted third error with an accumulation of instantaneous vehicle position and heading errors obtained via a comparison between a camera-observed feature and a corresponding feature from a digital map.

Vehicle pose estimation

Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Systems and methods for transforming autonomous aerial vehicle sensor data between platforms

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.

Image rectification

A system comprises a computer that includes a processor and a memory. The memory stores instructions executable by the processor to estimate a first joint probability distribution of first data with respect to second data based on first and second marginal probability distributions of the first and second data, wherein the first data is from a first sensor based on a first sensor coordinate system, and the second data is from a second sensor based on a second sensor coordinate system, to estimate a second joint probability distribution of the first data with respect to the second data based on a projection of the first data onto a plane defined in the second sensor coordinate system, to estimate a rigid transformation between the first sensor coordinate system and the second sensor coordinate system by minimizing a distance between the first and second joint probability distributions, wherein the second joint probability distribution is a function of a set of extrinsic calibration parameters, and based on the set of the extrinsic calibration parameters, to detect an object in the first and second data.

Sensor fusion

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Santa Clara, CA, United States of America

Ganesh Kumar