COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields. We project LiDAR intensity returns into an image, and present a novel image processing pipeline that produces filtered images with improved brightness consistency within the image as well as across different scenes. We effectively leverage intensity as an additional modality, using our new feature selection scheme that detects uninformative directions in the point cloud registration and explicitly selects patches with complementary image information. Photometric error minimization in the image patches is then fused with inertial measurements and point-to-plane registration in an iterated Extended Kalman Filter. The proposed approach improves accuracy and robustness on a public dataset. We additionally publish a new dataset, that captures five real-world environments in challenging, geometrically degenerate scenes. By using the additional photometric information, our approach shows drastically improved robustness against geometric degeneracy in environments where all compared baseline approaches fail.
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Publication status
published
Editor
Book title
2024 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
1730 - 1737
Publisher
IEEE
Event
41st IEEE International Conference on Robotics and Automation (ICRA 2024)
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Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Funding
-- - NCCR Digital Fabrication (SNF)
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