Proceedings ArticleDOI
Robust localization and localizability estimation with a rotating laser scanner
Weikun Zhen,Sam Zeng,Sebastian Soberer +2 more
- pp 6240-6245
TLDR
This paper presents a robust localization approach that fuses measurements from inertial measurement unit (IMU) and a rotating laser scanner and proposes a new method to evaluate localizability of a given 3D map and shows that the computedLocalizability can precisely predict localization errors, thus helps to find safe routes during flight.Abstract:
This paper presents a robust localization approach that fuses measurements from inertial measurement unit (IMU) and a rotating laser scanner. An Error State Kalman Filter (ESKF) is used for sensor fusion and is combined with a Gaussian Particle Filter (GPF) for measurements update. We experimentally demonstrated the robustness of this implementation in various challenging situations such as kidnapped robot situation, laser range reduction and various environment scales and characteristics. Additionally, we propose a new method to evaluate localizability of a given 3D map and show that the computed localizability can precisely predict localization errors, thus helps to find safe routes during flight.read more
Citations
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Journal Article
IEEE International Conference on Robotics and Automation (ICRA) におけるフルードパワー技術の研究動向
Journal ArticleDOI
FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
TL;DR: A computationally efficient and robust LiDAR-inertial odometry framework that fuse LiDar feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs.
Proceedings ArticleDOI
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation
TL;DR: Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order- of-magnitude improvement in speed.
Proceedings ArticleDOI
Estimating the Localizability in Tunnel-like Environments using LiDAR and UWB
Weikun Zhen,Sebastian Scherer +1 more
TL;DR: A novel degeneration characterization model is presented to estimate the localizability at a given location in the prior map, and a probabilistic sensor fusion method is developed to combine IMU, LiDAR and the UWB.
Journal ArticleDOI
An Approach to Robust INS/UWB Integrated Positioning for Autonomous Indoor Mobile Robots
TL;DR: An effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots is proposed and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed, in which the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly.
References
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Book
Probabilistic Robotics
TL;DR: This research presents a novel approach to planning and navigation algorithms that exploit statistics gleaned from uncertain, imperfect real-world environments to guide robots toward their goals and around obstacles.
Journal ArticleDOI
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Proceedings ArticleDOI
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Ji Zhang,Sanjiv Singh +1 more
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An Efficient Probabilistic 3D Mapping Framework Based on Octrees
TL;DR: In this paper, an open-source framework is presented to generate volumetric 3D environ- ment models based on octrees and uses probabilistic occupancy estimation, which explicitly repre- sents not only occupied space, but also free and unknown areas.
Proceedings ArticleDOI
Visual-lidar odometry and mapping: low-drift, robust, and fast
Ji Zhang,Sanjiv Singh +1 more
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