JRM Vol.27 No.1 pp. 64-73
doi: 10.20965/jrm.2015.p0064


Reduction of Distance Drift with Temperature in Uniaxial Laser Rangefinder by Using Multiecho Sensing

Kouhei Ito*, Akihisa Ohya**, Naohiro Shimaji***, and Takeshi Aoki***

*Kanazawa Technical College
2-270 Hishayasu, Kanazawa, Ishikawa 921-8601, Japan

**University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

***Hokuyo Automatic Co., Ltd.
1-37 Kamisucho, Toyonaka, Osaka 561-0823, Japan

August 11, 2014
December 28, 2014
February 20, 2015
uniaxial laser rangefinder, measurement drift, multiecho sensing
Uniaxial laser rangefinder
To expand the range of activities by unmanned aerial vehicles (UAVs), whose use at disaster sites and other dangerous locations has raised high expectations, UAVs must be capable of several functions, e.g., flight both indoor and outdoor environments. This requires making the airframe and major sensors as compact and light-weight as possible. To do so, we are developing uniaxial laser rangefinders to replace conventional scanning laser rangefinders. One developmental approach to uniaxial laser rangefinders and results regarding their validity are reported in this paper. Using a simulated model -- a modified scanning laser rangefinder -- we discuss the effectiveness of our approach. Specifically, this is to reduce the range drift due to heat, for which we propose using a multiecho to measure two echoes from a reference plate whose distance is known and that is placed outside of the sensor, and the target object, instead using the internal reference plate used in conventional two-dimensional scanning laser rangefinders. We also report the results of experiments verifying its validity.
Cite this article as:
K. Ito, A. Ohya, N. Shimaji, and T. Aoki, “Reduction of Distance Drift with Temperature in Uniaxial Laser Rangefinder by Using Multiecho Sensing,” J. Robot. Mechatron., Vol.27 No.1, pp. 64-73, 2015.
Data files:
  1. [1] S. Thrun, D. Fox, W. Burgard, and F. Dellaert, “Robust monte carlo localization for mobile robots,” Artificial intelligence, Vol.128, No.1, pp. 99-141, 2001.
  2. [2] D. Hahnel, W. Burgard, D. Fox, and S. Thrun, “An efficient fastslam algorithm for generating maps of large-scale cyclic environments from raw laser range measurements,”
  3. [3] Proc. 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2003 (IROS 2003), Vol.1, pp. 206-211, 2003.
  4. [4] A. Nüuchter, K. Lingemann, J. Hertzberg, and H. Surmann, “6D SLAM--3D mapping outdoor environments,” J. of Field Robotics, Vol.24, No.8-9, pp. 699-722, 2007.
  5. [5] D. Iwakura and K. Nonami, “Indoor localization of flying robot by means of infrared sensors,” J. of Robotics and Mechatronics, Vol.25, No.1, pp. 201-210, 2013.
  6. [6] T. Mori, M. Hino, T. Kamitani, and S. Yuta, “Development of AM and Pulsed SOKUIKI SENSOR using TOF principle,” Proc. 2007 Annual Conf. of the Robotics Society of Japan (RSJ 2007), Vol.1, 2A13, 2007.
  7. [7] S. Manhart, P. Dyrna, and B. Kunkel, “Ranging method and apparatus,” US Patent 4,770,526, September 13, 1988.

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