ArticleSensors (Basel, Switzerland)2018
A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm.
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 15 papers.
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Who cites it
15 citing papers in PubMed, 83 citations in OpenAlex.
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- CNN-Based Fault Detection of Scan Matching for Accurate SLAM in Dynamic Environments.Sensors (Basel, Switzerland) · 2023Article
- Experimental Analysis of Various Blockage Performance for LiDAR Sensor Cleaning Evaluation.Sensors (Basel, Switzerland) · 2023Article
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- High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.Sensors (Basel, Switzerland) · 2020Article
- Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness Weighting.Sensors (Basel, Switzerland) · 2019Article
- An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors.Sensors (Basel, Switzerland) · 2019Article
- A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM.Sensors (Basel, Switzerland) · 2019Article
- Application of an Array of Metal-Oxide Semiconductor Gas Sensors in an Assistant Personal Robot for Early Gas Leak Detection.Sensors (Basel, Switzerland) · 2019Article
- Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.