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

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.

• Record URL:
  • https://doi.org/10.3390/s17061268
• Record URL:
  • http://www.mdpi.com/1424-8220/17/6/1268
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2017 G. Ajay Kumar, et al.
• Authors:
  • Kumar, G Ajay
  • Patil, Ashok Kumar
  • Patil, Rekha
  • Park, Seong Sill
  • Chai, Young Ho
• Publication Date: 2017-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 24p
• Serial:
  • Sensors
  • Volume: 17
  • Issue Number: 6
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Classification; Drones; Laser radar; Navigation; Pipelines; Real time information; Tracking systems
• Uncontrolled Terms: Inertial measurement units
• Subject Areas: Aviation; Pipelines; Vehicles and Equipment;

Filing Info

• Accession Number: 01666705
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 23 2018 4:44PM