Here Global B.v.

Matt Mott

Anirudh Viswanathan

Methods described herein relate to iteratively refining parameters of a localization framework for different data sources. Methods may include: receiving a map data set from a data source; applying an offset to the map data to generate an offset map data set; applying a localization framework between the map data set and the offset map data set to generate a localized data set; recovering offset data from the localized data set; iteratively tuning parameters of the localization framework in response to the offset data from the localized data set failing to correspond with the applied offset until offset data recovered from the localized data set corresponds to the applied offset; receiving sensor data from at least one sensor of a vehicle; applying the localization framework to the sensor data to locate the vehicle within a mapped region.

Method and apparatus for iterative refinement of parameters of a localization framework

An approach is provided for localizing a vehicle pose on a map. The approach involves, receiving an input specifying the vehicle pose with respect to a road lane of the map. The approach also involves searching over a set of candidate lateral offsets to select a lateral offset that minimizes a lateral error between the vehicle position with the lateral offset applied and a lateral location of the road lane, wherein the lateral location and the travel direction of the lane are determined from the map. The approach further involves searching over a set of candidate vehicle headings at the selected lateral offset to select a vehicle heading that minimizes a heading error. The approach further involves determining a local optimum of the vehicle pose based on the selected lateral offset and vehicle heading, wherein the vehicle pose is localized to the map based on the local optimum.

Method and apparatus for estimating a localized position on a map

A method is provided for generating a representation of an environment. Methods may include: determining location information of a vehicle including a road segment and a direction of travel; identifying features of the road segment based on sensor data from sensors carried by the vehicle; projecting the features of the road segment onto a ground plane of the road segment; defining bins across a width of the road segment; laterally positioning the defined bins relative to a determination of positions of the features of the road segment; consolidating detected features of each bin to define features of the road segment; and guiding an autonomous vehicle along the road segment based, at least in part, on the consolidated detected features of the road segment.

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Berkeley, CA, United States of America

Matt Mott