A LiDAR and IMU Integrated Indoor Navigation System for UAVs and ItsApplication in Real-Time Pipeline Classification

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473683.do
DOI
10.3390/s17061268
Title
A LiDAR and IMU Integrated Indoor Navigation System for UAVs and ItsApplication in Real-Time Pipeline Classification
Description
This work was supported by the MSIP of Korea, under the Global IT Talentsupport program (IITP-2015-R0110-15-2003) supervised by the IITP and bya National Research Foundation of Korea (NRF) grant funded by the Koreangovernment (MEST) (NRF-2016R1D1A1B03930795).
abstract
Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering.
provenance
Made available in Cube on 2018-09-28T10:26:07Z (GMT). No. of bitstreams: 0
language
English
author
Kumar, G. Ajay
Patil, Ashok Kumar
Patil, Rekha
Park, Seong Sill
Chai, Young Ho
orcid
KUMAR, G AJAY/0000-0003-1182-7241; chai, youngho/0000-0003-0513-7471
accessioned
2018-09-28T10:26:07Z
available
2018-09-28T10:26:07Z
issued
2017
citation
SENSORS(17): 6
issn
1424-8220
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473683.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274104
Jurisdiction
Rep.of Korea
Project Name
Professional Graduate Institute for Global Creative Contents
rights
openAccess
subject
scan matching
indoor navigation
indoor mapping
indoor UAV tracking
3D model reconstruction
pipeline
classification
type
article


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