Ultra-wideband aided fast localization and mapping system

Wang, Chen; Zhang, Handuo; Nguyen, Thien‐Minh; Xie, Lihua

doi:10.1109/iros.2017.8205968

Cited by 80 publications

(58 citation statements)

References 18 publications

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“…[16] proposes a probabilistic model to fuse sparse 3D LiDAR data with stereo images to provide accurate dense depth maps and uncertainty estimates in realtime. [4] proposes to fuse UWB and visual inertial odometry to remove the visual drift with the aid of UWB ranging measurements thus improving the robustness of system. This system is anchor-based where our system is anchor-less.…”

Section: Uwb-only Slam Scan Matchingmentioning

confidence: 99%

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UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Song

Guan

Tay

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We equip an ultra-wideband (UWB) node and a 2D LiDAR sensor a.k.a. 2D laser rangefinder on a mobile robot, and place UWB beacon nodes at unknown locations in an unknown environment. All UWB nodes can do ranging with each other thus forming a cooperative sensor networks. We propose to fuse the peer-to-peer ranges measured between UWB nodes and laser scanning information, i.e. range measured between robot and nearby objects/obstacles, for simultaneous localization of the robot, all UWB beacons and LiDAR mapping. The fusion is inspired by two facts: 1) LiDAR may improve UWB-only localization accuracy as it gives more precise and comprehensive picture of the surrounding environment; 2) on the other hand, UWB ranging measurements may remove the error accumulated in the LiDAR-based SLAM algorithm. Our experiments demonstrate that UWB/LiDAR fusion enables driftfree SLAM in real-time based on ranging measurements only.

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Section: Uwb-only Slam Scan Matchingmentioning

confidence: 99%

“…This approach relies heavily on accuracy of the robot's pose estimate. One another way to remove accumulated errors and enhance the robustness in LiDAR-based SLAM is by sensor fusion [4]. In this paper, we propose to fuse LiDAR sensor with UWB sensors.…”

Section: Introductionmentioning

confidence: 99%

UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Song

Guan

Tay

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Section: Related Workmentioning

confidence: 99%

“…The majority of previous works on indoor localization utilized the conventional anchor configuration [10]- [12] to solve (i). Reference [10] combines UWB sensors with a visual inertial system to correct drifts when building maps. The authors in [12] utilize particle filtering to handle the multimodal error behavior of the non-line-of-sight (NLOS) UWB measurements.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure-free Multi-robot Localization with Ultrawideband Sensors

Güler¹,

Abdelkader²,

Shamma³

2019

2019 American Control Conference (ACC)

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Robots in a swarm take advantage of a motion capture system or GPS sensors to obtain their global position. However, motion capture systems are environment-dependent and GPS sensors are not reliable in occluded environments. For a reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, here we propose an on-board localization framework for multirobot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with an acceptable precision. We validate the effectiveness of our algorithm with simulations and indoor and outdoor experiments on a two-drone setup. The proposed dual MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates a superior performance over the standard particle filter and the extended Kalman Filter.

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“…To be detailed, the algorithm is executed in real-time on an on-board computer running Ubuntu and Robot Operating System (ROS). Noticing that UWB is robust to multipath and non-line-of-sight effects, and provides a reliable long distance ranging with an error within only a few centimeters [3], [17], [18], we obtain distance measurements by using two UWB nodes, with one mounted on a so-called target UAV hovering at the destination, and the other one installed on the autonomous UAV.…”

Section: Experiments On Quadcoptersmentioning

confidence: 99%

An Integrated Localization-Navigation Scheme for Distance-Based Docking of UAVs

Nguyen

Qiu

Cao

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper we study the distance-based docking problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously achieve the relative localization and navigation tasks for discrete-time integrators under bounded velocity: a nonlinear adaptive estimation scheme to estimate the relative position to the landmark, and a delicate control scheme to ensure both the convergence of the estimation and the asymptotic docking at the given landmark. A rigorous proof of convergence is provided by invoking the discrete-time LaSalle's invariance principle, and we also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a GPS-less environment.The authors are with the

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Ultra-wideband aided fast localization and mapping system

Cited by 80 publications

References 18 publications

UWB/LiDAR Fusion For Cooperative Range-Only SLAM

UWB/LiDAR Fusion For Cooperative Range-Only SLAM

Infrastructure-free Multi-robot Localization with Ultrawideband Sensors

An Integrated Localization-Navigation Scheme for Distance-Based Docking of UAVs

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