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Deep Voxelized Feature Maps for Self-Localization in Autonomous Driving.

Yuki Endo1, Shunsuke Kamijo2

  • 1Department of Information & Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 153-8505, Japan.

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Summary
This summary is machine-generated.

This study introduces voxelized deep feature maps for more accurate autonomous driving self-localization. This new map format offers improved efficiency and reduced storage needs compared to traditional point cloud maps.

Keywords:
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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Autonomous driving relies on precise lane-level self-localization.
  • Traditional point cloud maps are data-intensive and redundant.
  • Direct use of deep features for mapping can degrade performance in large-scale environments.

Purpose of the Study:

  • To propose a novel and practical map format for self-localization using deep features.
  • To enhance the accuracy and efficiency of autonomous vehicle localization.
  • To address the limitations of existing map representations.

Main Methods:

  • Development of voxelized deep feature maps, where deep features are localized within small regions.
  • Implementation of a self-localization algorithm incorporating per-voxel residual and scan point reassignment.
  • Comparative experimental analysis against point cloud maps and standard feature maps.

Main Results:

  • The proposed voxelized deep feature map achieved superior self-localization accuracy.
  • The new map format demonstrated improved efficiency and reduced storage requirements.
  • Experimental validation confirmed the effectiveness of the per-voxel optimization approach.

Conclusions:

  • Voxelized deep feature maps represent a practical and effective solution for lane-level self-localization.
  • This approach offers a significant advancement over existing methods for autonomous driving.
  • The proposed method balances accuracy, efficiency, and storage demands.