11 papers
3DRO: Lidar-level SE(3) Direct Radar Odometry Using a 2D Imaging Radar and a Gyroscope
Cedric Le Gentil, Daniil Lisus, Timothy D. Barfoot
Recently, the robotics community has regained interest in radar-based perception and state estimation. A 2D imaging radar provides dense 360deg information about the environment. D…
Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Daniil Lisus, Cedric Le Gentil, Timothy D. Barfoot
This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images. Unlike camera or lidar sensors…
Balancing Act: Trading Off Odometry and Map Registration for Efficient Lidar Localization
Katya M. Papais, Daniil Lisus, Cedric Le Gentil +2
Most autonomous vehicles rely on accurate and efficient localization, which is achieved by comparing live sensor data to a preexisting map, to navigate their environment. Balancing…
Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
Daniil Lisus, Katya M. Papais, Cedric Le Gentil +4
The Boreas Road Trip (Boreas-RT) dataset extends the multi-season Boreas dataset to new and diverse locations that pose challenges for modern autonomous driving algorithms. Boreas-…
2Fast-2Lamaa: Large-Scale Lidar-Inertial Localization and Mapping with Continuous Distance Fields
Cedric Le Gentil, Raphael Falque, Daniil Lisus +1
This paper introduces 2Fast-2Lamaa, a lidar-inertial state estimation framework for odometry, mapping, and localization. Its first key component is the optimization-based undistort…
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm
Ziyu Zhang, Johann Laconte, Daniil Lisus +1
This paper presents a novel method for assessing the resilience of the ICP algorithm via learning-based, worst-case attacks on lidar point clouds. For safety-critical applications…