20 citations · 42 across the 14 of their papers we have counts for
18 papers
Dr-LiSA: Direct Radar-Lidar Scan Alignment for SE(3) Localization
Alex Zhang, Daniil Lisus, Cedric Le Gentil +1
This paper introduces Dr-LiSA, a first-of-its-kind direct method for localizing 2D spinning radar intensity measurements in against 3D lidar maps. Radar-lidar localization…
Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?
Eric Xie, Daniil Lisus, Timothy D. Barfoot
Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather condition…
DRT&R: Direct Radar Teach & Repeat
Alexander Krawciw, Daniil Lisus, Cedric Le Gentil +1
Radar-based navigation is appealing for its robustness to adverse conditions involving airborne particles, such as precipitation, dust, fog, and smoke, that can cause lidar-based s…
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…
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-…
Do We Still Need to Work on Odometry for Autonomous Driving?
Cedric Le Gentil, Daniil Lisus, Timothy D. Barfoot
Over the past decades, a tremendous amount of work has addressed the topic of ego-motion estimation of moving platforms based on various proprioceptive and exteroceptive sensors. A…
DRO: Doppler-Aware Direct Radar Odometry
Cedric Le Gentil, Leonardo Brizi, Daniil Lisus +3
A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls…
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…
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…
The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry
Elliot Preston-Krebs, Daniil Lisus, Timothy D. Barfoot
A key element of many odometry pipelines using spinning frequency-modulated continuous-wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction gre…
Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?
Daniil Lisus, Keenan Burnett, David J. Yoon +3
Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automot…
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…
Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
Daniil Lisus, Johann Laconte, Keenan Burnett +2
This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. This radar-lidar localization leverages the benefits of both s…
Toward Certifying Maps for Safe Registration-based Localization Under Adverse Conditions
Johann Laconte, Daniil Lisus, Timothy D. Barfoot
In this paper, we propose a way to model the resilience of the Iterative Closest Point (ICP) algorithm in the presence of corrupted measurements. In the context of autonomous vehic…
Towards Open World NeRF-Based SLAM
Daniil Lisus, Connor Holmes, Steven Waslander
Neural Radiance Fields (NeRFs) offer versatility and robustness in map representations for Simultaneous Localization and Mapping (SLAM) tasks. This paper extends NICE-SLAM, a recen…
Know What You Don't Know: Consistency in Sliding Window Filtering with Unobservable States Applied to Visual-Inertial SLAM (Extended Version)
Daniil Lisus, Mitchell Cohen, James Richard Forbes
Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservabl…
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes
Daniil Lisus, Charles Champagne Cossette, Mohammed Shalaby +1
It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environ…