Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Xieyuanli Chen"'
Publikováno v:
IEEE Robotics and Automation Letters. 6:8221-8228
The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a novel and flexible graph networ
Autor:
Louis Wiesmann, Jürgen Gall, Xieyuanli Chen, Benedikt Mersch, Jens Behley, Shijie Li, Cyrill Stachniss
Publikováno v:
IEEE Robotics and Automation Letters (RA-L)
The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this paper, we address the problem of moving object segmentation from 3D LiD
Publikováno v:
IEEE Robotics and Automation Letters. 6:2060-2067
Many modern robotics applications rely on 3D maps of the environment. Due to the large memory requirements of dense 3D maps, compression techniques are often necessary to store or transmit 3D maps efficiently. In this work, we investigate the problem
Autor:
Kaihong Huang, Chenghao Shi, Xieyuanli Chen, Hui Zhang, Ruibin Guo, Wenbang Deng, Zhiqian Zhou
Publikováno v:
IEEE Access, Vol 8, Pp 221320-221329 (2020)
In this paper, we propose a semantic simultaneous localization and mapping (SLAM) framework for rescue robots, and report its use in navigation tasks. Our framework can generate not only geometric maps in the form of dense point-clouds but also corre
Publikováno v:
ECMR
European Conference on Mobile Robots (ECMR)
2021 European Conference on Mobile Robots (ECMR)
European Conference on Mobile Robots (ECMR)
2021 European Conference on Mobile Robots (ECMR)
Reliable and accurate localization is crucial for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, lamps, etc., are ideal landmarks for localization in urban environments due to their local distinctiveness and long-term sta
Mapping the static parts of dynamic scenes from 3D LiDAR point clouds exploiting ground segmentation
Publikováno v:
European Conference on Mobile Robots (ECMR)
ECMR
2021 European Conference on Mobile Robots (ECMR)
ECMR
2021 European Conference on Mobile Robots (ECMR)
Dynamic objects are an inherent part of our world, but their presence deteriorates the performance of various localization, navigation, and SLAM algorithms. This not only makes it important but necessary to remove these dynamic points from the map be
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA)
2021 IEEE International Conference on Robotics and Automation (ICRA)
ICRA
2021 IEEE International Conference on Robotics and Automation (ICRA)
ICRA
Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or LiDARs. In this work, we present a resilient approach that exploits t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3761258c227504b375a37b51b992d8e4
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39d1cd07c5cb37b48f5166b5d5e795d0
Publikováno v:
ICRA
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1c6a94674c3b3d6e18c3b9780a65769
Autor:
Chenghao Shi, Hui Zhang, Ruibin Guo, Zhiqian Zhou, Wenbang Deng, Xieyuanli Chen, Kaihong Huang
Publikováno v:
2020 Chinese Automation Congress (CAC).
Semantic information has proven to be an enabling factor for robots to better understand their surroundings. In this paper, we propose an RGB-D based semantic simultaneous localization and mapping (SLAM) framework for rescue robots. By augmenting the