Authors: Kenji Koide, Shuji Oishi, Masashi Yokozuka, Atsuhiko Banno

Published on: February 08, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.05540

Summary

• What is new: A new algorithm for inertial localization that tightly couples scan-to-scan and scan-to-map registration with IMU data for improved tracking in challenging conditions.
• Why this is important: Existing localization methods struggle with severe point cloud degeneration, sensor interruptions, and navigating unmapped regions.
• What the research proposes: Tightly coupled scan-to-scan and scan-to-map registration factors with IMU factors, utilizing a sliding window factor graph for enhanced sensor pose tracking.
• Results: The proposed method surpasses current leading techniques, especially under extreme conditions such as degenerate point cloud data and sensor disruptions.

Technical Details

Technological frameworks used: Sliding window factor graph

Models used: Scan-to-Scan and Scan-to-Map point cloud registration, IMU factors coupling

Data used: 3D prior maps, inertial measurement unit (IMU) data

Potential Impact

Autonomous vehicle navigation, augmented reality applications, mobile robotics, and companies specializing in mapping and spatial analytics technologies.

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