Static LiDAR Assisted UWB Anchor Nodes Localization

Kołakowski, Marcin; Djaja-Jośko, Vitomir; Kołakowski, Jerzy

doi:10.1109/jsen.2020.3046306

Cited by 13 publications

(8 citation statements)

References 19 publications

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“…In [26], the poor estimation along the vertical axis, which is a known weakness of the UWB infrastructures, is mitigated using inter-vehicle distance. In [27], a combination of LiDAR (Light Detection and Ranging) and UWB is investigated, exploiting LiDAR information to improve the UWB results.…”

Section: Introductionmentioning

confidence: 99%

On-Line Optimal Ranging Sensor Deployment for Robotic Exploration

2022

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Navigation in an unknown environment without any preexisting positioning infrastructure has always been hard for mobile robots. This paper presents a self-deployable ultra wideband UWB infrastructure by mobile agents, that permits a dynamic placement and runtime extension of UWB anchors infrastructure while the robot explores the new environment. We provide a detailed analysis of the uncertainty of the positioning system while the UWB infrastructure grows. Moreover, we developed a genetic algorithm that minimizes the deployment of new anchors, saving energy and resources on the mobile robot and maximizing the time of the mission. Although the presented approach is general for any class of mobile system, we run simulations and experiments with indoor drones. Results demonstrate that maximum positioning uncertainty is always controlled under the user's threshold, using the Geometric Dilution of Precision (GDoP).

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Section: Introductionmentioning

confidence: 99%

On-Line Optimal Ranging Sensor Deployment for Robotic Exploration

2022

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“…The localization of a robot is usually described as the state of it, including position and posture in the relevant map [ 4 ]. Some technologies used to solve this problem, such as ultra-wide band (UWB) [ 5 , 6 ], Bluetooth [ 7 , 8 ], etc., need to rely on the previous resource deployment for environmental scenes. However, a fully intelligent mobile robot is often required to work normally in an unknown environment.…”

Section: Introductionmentioning

confidence: 99%

An Improved FAST Algorithm Based on Image Edges for Complex Environment

Ding

2022

Sensors

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In complex environments such as those with low textures or obvious brightness changes, point features extracted from a traditional FAST algorithm cannot perform well in pose estimation. Simultaneously, the number of point features extracted from FAST is too large, which increases the complexity of the build map. To solve these problems, we propose an L-FAST algorithm based on FAST, in order to reduce the number of extracted points and increase their quality. L-FAST pays more attention to the intersection of line elements in the image, which can be extracted directly from the related edge image. Hence, we improved the Canny edge extraction algorithm, including denoising, gradient calculation and adaptive threshold. These improvements aimed to enhance the sharpness of image edges and effectively extract the edges of strong light or dark areas in the images as brightness changed. Experiments on digital standard images showed that our improved Canny algorithm was smoother and more continuous for the edges extracted from images with brightness changes. Experiments on KITTI datasets showed that L-FAST extracted fewer point features and increased the robustness of SLAM.

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“…However, thanks to machine learning and other techniques [43][44][45][46][47], the effect of detrimental multipath or non-line-of-sight (NLOS) conditions can be mitigated. Alternatively, multi-sensor data fusion, also known as sensor fusion, can be used to help the UWB system in the localization task, as proposed in this paper [48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

“…By generally speaking, sensor fusion localization schemes foresee combining the sensed data from different technologies to obtain a more accurate and reliable location estimation. The fused sensors can be of different natures, and it is common to find in the literature solutions that combine together several exteroceptive sensors [49][50][51][52][53][54]. However, the installation of multiple exteroceptive sensor systems can be wasteful, in terms of time, cost, and computational burden.…”

Section: Introductionmentioning

confidence: 99%

Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion

2021

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This article addresses the problem of determining the location of pallets carried by forklifts inside a warehouse, which are recognized thanks to an onboard Radio Frequency IDentification (RFID) system at the ultra-high-frequency (UHF) band. By reconstructing the forklift trajectory and orientation, the location of the pallets can be associated with the forklift position at the time of unloading events. The localization task is accomplished by means of an easy-to-deploy combination of onboard sensors, i.e., an inertial measurement unit (IMU) and an optical flow sensor (OFS), with a commercial ultra-wideband (UWB) system through an Unscented Kalman Filter (UKF) algorithm, which estimates the forklift pose over time. The proposed sensor fusion approach contributes to the localization error mitigation by preventing drifts in the trajectory reconstruction. The designed methos was at first evaluated by means of a simulation framework and then through an experimental analysis conducted in a large warehouse with a size of about 4000 m2.

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Static LiDAR Assisted UWB Anchor Nodes Localization

Cited by 13 publications

References 19 publications

On-Line Optimal Ranging Sensor Deployment for Robotic Exploration

On-Line Optimal Ranging Sensor Deployment for Robotic Exploration

An Improved FAST Algorithm Based on Image Edges for Complex Environment

Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion

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