Zoox, Inc.

Veeresh Taranalli

Michael Carsten Bosse

Elena Stephanie Stumm

Derek Adams

Till Kroeger

Techniques are described for modifying a factor associated with an invalid point cloud registration (e.g., alignment) between two data sets, to improve a map generation process. A computing system receives sensor data from a vehicle traversing an environment. The computing system aligns discrete data sets, such as to generate a representation of the environment based on the discrete data sets. In some examples, the computing system can apply an optimization algorithm to the alignments, to optimize the representation of the environment. The computing system identifies an invalid (non-ideal) alignment between two data sets and modifies a factor associated with the invalid alignment, introducing a repulsive force. The computing system performs an optimization with the modified factor, which causes a location associated with at least one of the two data sets to be different from a previously measured location. The computing system generates a map based on the optimization.

Aligning data for mapping

Techniques associated with generating and maintaining sparse geographic and map data. In some cases, the system may maintain a factor graph comprising a plurality of nodes. In some cases, the nodes may comprise pose data and sensor data associated with an autonomous vehicle at the geographic position represented by the node. The nodes may be linked based on shared trajectories and shared sensor data.

System for sparsely representing and storing geographic and map data

A soft-constraint technique for refining an initial pose graph may eschew using a hard constraint that identifies different sensor data and/or poses as necessarily being associated with a same portion of an environment. Instead, the soft-constraint technique may employ a loss function with a convergence basin that may be defined based at least in part on an object classification that strongly penalizes candidate locations within a distance associated with the convergence basin. These candidate locations may be based at least in part on one or more object detections associated (1:1) with one or more poses of the initial pose graph. This may result in one or more candidate locations that do not merge with other candidate locations, giving the pose graph optimization the permissiveness or softness according to the techniques described herein.

Secondary sensor data-based soft constraint optimization for pose graph and mapping refinement

Techniques are described for determining whether a point cloud registration (e.g., alignment) between two sets of data points is valid. Such techniques can include determining voxelized representations of the sets of data points, and comparing characteristics of spatially aligned voxels within the voxelized representations. Characteristics of voxels to be compared can include classification labels of data points associated with voxels, including whether or not voxels correspond to free space. Point cloud registrations determined to be invalid can be given a weighting to be used in a subsequent high definition (HD) map building process. Generated maps can then be deployed for use in autonomous vehicles.

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Foster City, CA, United States of America

Veeresh Taranalli