Lyft, Inc.

Karim Tarek Mahmoud Elsayed Ahmed Shaban

Luca Del Pero

Lukas Platinsky

Lei Zhang

Li Jiang

Examples disclosed herein may involve a computing system that is operable to (i) generate a local map portion of a geographical environment based on sensor data captured by a device, wherein the local map portion comprises local map structure data generated using one or more map structure generation methods, (ii) determine a transformation of the local map structure data relative to existing map structure data of an existing map based on common visible features between the local map structure data and the existing map structure data, wherein the existing map structure data is aligned to a global coordinate system and is predetermined from a plurality of previously-generated map structure data, and (iii) determine a localization of the device within the global coordinate system using the determined transformation.

Localization based on multi-collect fusion

Examples disclosed herein involve a computing system configured to (i) obtain first image data captured by a first camera of a vehicle during a given period of operation of the vehicle, (ii) obtain second image data captured by a second camera of the vehicle during the given period of operation, (iii) based on the obtained first and second image data, determine (a) a candidate extrinsics transformation between the first camera and the second camera and (b) a candidate time offset between the first camera and the second camera, and (iv) based on (a) the candidate extrinsics transformation and (b) the candidate time offset, apply optimization to determine a combination of (a) an extrinsics transformation and (b) a time offset that minimizes a reprojection error in the first image data, where the reprojection error is defined based on a representation of at least one landmark that is included in both the first and second image data.

Camera to camera calibration

Examples disclosed herein involve a computing system configured to (i) obtain first sensor data captured by a first sensor of a vehicle during a given period of operation of the vehicle (ii) obtain second sensor data captured by a second sensor of the vehicle during the given period of operation of the vehicle, (iii) based on the first sensor data, localize the first sensor within a first coordinate frame of a first map layer, (iv) based on the second sensor data, localize the second sensor within a second coordinate frame of a second map layer, (v) based on a known transformation between the first coordinate frame and the second coordinate frame, determine respective poses for the first sensor and the second sensor in a common coordinate frame, and (vi) determine (a) a translation and (b) a rotation between the respective poses for the first and second sensors in the common coordinate frame.

Validating vehicle sensor calibration

Examples disclosed herein may involve a computing system that is operable to (i) generate first structure data from one or more first image data, wherein the first structure data comprises one or more visible features captured in the one or more first image data, (ii) generate further structure data from one or more further image data, wherein the further structure data comprises one or more visible features captured in the one or more further image data, (iii) determine pose constraints for the further structure data based on common visible features, (iv) determine a transformation of the further structure data relative to the first structure data using the determined pose constraints, and (v) generate combined structure data using the determined transformation to fuse the further structure data and the first structure data.

Growing community of inventors

London, United Kingdom

Karim Tarek Mahmoud Elsayed Ahmed Shaban