Zobrazeno 1 - 10
of 640
pro vyhledávání: '"Cattaneo, Daniele"'
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perform sensor fusion in a single view by projecting features from both moda
Externí odkaz:
http://arxiv.org/abs/2410.07475
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to update models with emerging new classes while preventing catastrophic
Externí odkaz:
http://arxiv.org/abs/2407.18145
Autor:
Petek, Kürsat, Vödisch, Niclas, Meyer, Johannes, Cattaneo, Daniele, Valada, Abhinav, Burgard, Wolfram
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 9978-9985, November 2024
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highly accurate calibration between them. In this paper, we propose MDPCali
Externí odkaz:
http://arxiv.org/abs/2404.17298
Autor:
Cattaneo, Daniele, Valada, Abhinav
LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in LiDAR maps using inexpensive cameras is a cost-effective alternative
Externí odkaz:
http://arxiv.org/abs/2402.00129
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA)
Localization is paramount for autonomous robots. While camera and LiDAR-based approaches have been extensively investigated, they are affected by adverse illumination and weather conditions. Therefore, radar sensors have recently gained attention due
Externí odkaz:
http://arxiv.org/abs/2309.09875
Safety and efficiency are paramount in healthcare facilities where the lives of patients are at stake. Despite the adoption of robots to assist medical staff in challenging tasks such as complex surgeries, human expertise is still indispensable. The
Externí odkaz:
http://arxiv.org/abs/2308.03193
Publikováno v:
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems are known to generalize poorly to u
Externí odkaz:
http://arxiv.org/abs/2303.10149
Autor:
Fioravanti, Massimo, Cattaneo, Daniele, Terraneo, Federico, Seva, Silvano, Cherubin, Stefano, Agosta, Giovanni, Casella, Francesco, Leva, Alberto
Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative languages. When confronted with nowadays' problems, however, well assessed
Externí odkaz:
http://arxiv.org/abs/2212.11135
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1319-1326, March 2023
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based methods achieve this goal by using only the geometric information, disreg
Externí odkaz:
http://arxiv.org/abs/2209.09699
Publikováno v:
Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27, pp 19-35
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous Localization and Mapping (SLAM) being one of the fundamental tasks for
Externí odkaz:
http://arxiv.org/abs/2203.01578