ArticleSensors (Basel, Switzerland)2018
Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A CMOS Optoelectronic Transceiver with Concurrent Automatic Power Control for Short-Range LiDAR Sensors.Sensors (Basel, Switzerland) · 2025Article
- A Novel Multi-Sensor Nonlinear Tightly-Coupled Framework for Composite Robot Localization and Mapping.Sensors (Basel, Switzerland) · 2024Article
- Large-Scale Indoor Camera Positioning Using Fiducial Markers.Sensors (Basel, Switzerland) · 2024Article
- Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots.Sensors (Basel, Switzerland) · 2022Article
- Novel Solutions to the Three-Anchor ToA-Based Three-Dimensional Positioning Problem.Sensors (Basel, Switzerland) · 2021Article
- UAPF: A UWB Aided Particle Filter Localization For Scenarios with Few Features.Sensors (Basel, Switzerland) · 2020Article
- A Wavelet Scattering Feature Extraction Approach for Deep Neural Network Based Indoor Fingerprinting Localization.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
8 authors.
Funding
Abstract
In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.
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