Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Lisus, Daniil"'
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 greatly impacts the overall performance of point-cloud-based odometry. Th
Externí odkaz:
http://arxiv.org/abs/2409.12256
Autor:
Lisus, Daniil, Burnett, Keenan, Yoon, David J., Poulton, Richard, Marshall, John, Barfoot, Timothy D.
Publikováno v:
IEEE Robotics and Automation Letters, vol. 10, no. 1, pp. 224-231, Jan. 2025
Spinning, frequency-modulated continuous-wave (FMCW) radars with 360 degree coverage have been gaining popularity for autonomous-vehicle navigation. However, unlike `fixed' automotive radar, commercially available spinning radar systems typically do
Externí odkaz:
http://arxiv.org/abs/2404.01537
This paper presents a novel method to assess the resilience of the Iterative Closest Point (ICP) algorithm via deep-learning-based attacks on lidar point clouds. For safety-critical applications such as autonomous navigation, ensuring the resilience
Externí odkaz:
http://arxiv.org/abs/2403.05666
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 sensors; radar is resilient against adverse weather, while lidar produc
Externí odkaz:
http://arxiv.org/abs/2309.08731
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 vehicles, certifying the safety of the localization process poses a signifi
Externí odkaz:
http://arxiv.org/abs/2309.04251
Publikováno v:
2023 20th Conference on Robots and Vision (CRV), Montreal, QC, Canada, 2023, pp. 37-44
Neural Radiance Fields (NeRFs) offer versatility and robustness in map representations for Simultaneous Localization and Mapping (SLAM) tasks. This paper extends NICE-SLAM, a recent state-of-the-art NeRF-based SLAM algorithm capable of producing high
Externí odkaz:
http://arxiv.org/abs/2301.03102
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 6, pp. 3382-3389, June 2023
Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to ``k
Externí odkaz:
http://arxiv.org/abs/2212.06923
Publikováno v:
in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8387-8393, Oct. 2021
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 environments where magnetic distortions make magnetometer-based heading estim
Externí odkaz:
http://arxiv.org/abs/2109.04868
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