Verification Of Iterative Closest Point Alignments For Autonomous Vehicles

Aspects of the disclosure relate to training and using a model for verifying accuracy of ICP alignments or alignments between data points using an iterative closest point algorithm. For instance, a model may be trained using ICP alignment data, including alignments between an object appearing in LID...

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Hauptverfasser: Lauterbach, Christian, Chen, Mingcheng
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creator Lauterbach, Christian
Chen, Mingcheng
description Aspects of the disclosure relate to training and using a model for verifying accuracy of ICP alignments or alignments between data points using an iterative closest point algorithm. For instance, a model may be trained using ICP alignment data, including alignments between an object appearing in LIDAR sensor frames. The training may also include setting a definition for a trusted ICP alignment. In this regard, the model may be trained such that, n response to receiving additional LIDAR sensor frames and corresponding additional ICP alignment data, output a value indicative of whether the additional ICP alignment data is trusted according to the definition. The model may then be used to control a vehicle in an autonomous driving mode by determining whether alignment data for object determined using the ICP algorithm should be trusted.
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subjects ANALOGOUS ARRANGEMENTS USING OTHER WAVES
CONTROLLING
DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES
LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES
MEASURING
PHYSICS
RADIO DIRECTION-FINDING
RADIO NAVIGATION
REGULATING
SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
TESTING
title Verification Of Iterative Closest Point Alignments For Autonomous Vehicles
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