aiqbench analysis
This paper presents MOLA-LIO, a novel LiDAR-inertial odometry system designed for the COMFORT Localization Benchmark at IROS 2026. By integrating IMU data and optional legged kinematics, the architecture aims to improve localization accuracy in dynamic environments while maintaining real-time performance. The work represents a significant step forward in sensor fusion techniques critical for advanced autonomous navigation systems.
MOLA LiDAR-Inertial Odometry (MOLA-LIO) on the COMFORT Localization Benchmark
arXiv:2609.25813v1 Announce Type: new Abstract: This short report documents our entry to the COMFORT Localization Benchmark (IROS 2026), evaluated on the GrandTour dataset recorded with the Boxi payload. It extends MOLA-LO into a LiDAR-inertial system that also ingests IMU and, optionally, legged kinematic odometry. We describe the architecture, the streams consumed, the local protocol that selected the submitted configuration, and the measurements backing our real-time claim.
Abstract:This short report documents our entry to the COMFORT Localization Benchmark (IROS 2026), evaluated on the GrandTour dataset recorded with the Boxi payload. It extends MOLA-LO into a LiDAR-inertial system that also ingests IMU and, optionally, legged kinematic odometry. We describe the architecture, the streams consumed, the local protocol that selected the submitted configuration, and the measurements backing our real-time claim.
Robotics (cs.RO)
arXiv:2609.25813 [cs.RO]
(or arXiv:2609.25813v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25813 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 154 words
Related coverage
2Fast-2Lamaa: Large-Scale Lidar-Inertial Localization and Mapping with Continuous Distance Fields
arXiv · Sep 17, 2026
GR-LIO: A Local Ground-Aware LiDAR-Inertial Odometry System Using Body-to-Ground Height
arXiv · Oct 6, 2026
Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
arXiv · Sep 18, 2026
When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants
arXiv · Oct 12, 2026