Experimental review of distance sensors for indoor mapping

Midriem Mirdanies, Roni Permana Saputra

Abstract

One of the most important required ability of a mobile robot is perception. An autonomous mobile robot has to be able to gather information from the environment and use it for supporting the accomplishing task. One kind of sensor that essential for this process is distance sensor. This sensor can be used for obtaining the distance of any objects surrounding the robot and utilize the information for localizing, mapping, avoiding obstacles or collisions and many others. In this paper, some of the distance sensor, including Kinect, Hokuyo UTM-30LX, and RPLidar were observed experimentally. Strengths and weaknesses of each sensor were reviewed so that it can be used as a reference for selecting a suitable sensor for any particular application. A software application has been developed in C programming language as a platform for gathering information for all tested sensors. According to the experiment results, it showed that Hokuyo UTM-30LX results in random normally distributed error on measuring distance with average error 21.94 mm and variance 32.11. On the other hand, error measurement resulted by Kinect and RPLidar strongly depended on measured distance of the object from the sensors, while measurement error resulted by Kinect had a negative correlation with the measured distance and the error resulted by RPLidar sensor had a positive correlation with the measured distance. The performance of these three sensors for detecting a transparent object shows that the Kinect sensors can detect the transparent object on its effective range measurement, Hokuyo UTM-30LX can detect the transparent object in the distance more than equal to 200 mm, and the RPLidar sensor cannot detect the transparent object at all tested distance. Lastly, the experiment shows that the Hokuyo UTM-30LX has the fastest processing time significantly, and the RPLidar has the slowest processing time significantly, while the processing time of Kinect sensor was in between. These processing times were not significantly affected by various tested distance measurement.




Keywords


distance sensors; Kinect; Hokuyo UTM-30LX; RPLidar; indoor mapping; autonomous mobile robot; C programming

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References


P. Henry et al., “RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments,” The International Journal of Robotics Research, vol. 31, no. 5, pp. 647-663, 2012. crossref

J. L. Raheja, A. Chaudhary dan K. Singal, “Tracking of Fingertips and Centers of Palm Using KINECT,” in Third International Conference on Computational Intelligence, Modelling & Simulation, Langkawi, 2011. crossref

M. Mirdanies, A. S. Prihatmanto dan E. Rijanto, “Object Recognition System in Remote Controlled Weapon Station using SIFT and SURF Methods,” Methatronics, Electrical Power, and Vehicular Technology, vol. 4, no. 2, pp. 99-108, 2013. crossref

N. Pouliot, P.-L. Richard and S. Montambault, “LineScout power line robot: Characterization of a UTM-30LX LIDAR system for obstacle detection,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, 2012. crossref

J. Zhang et al., “A real-time method for depth enhanced visual odometry,” Auton. Robots, vol. 41, no. 1, pp. 31–43, Jan. 2017. crossref

A. M. Markom, A. S. Abdul Shukor dan H. A. Adom, “Indoor Scanning and Mapping using Mobile Robot and RP Lidar,” Int'l Journal of Advances in Mechanical & Automobile Engg. (IJAMAE), vol. 3, no. 1, pp. 42-47, 2016. crossref

A. M. Markom et al., “A mapping mobile robot using RP Lidar Scanner,” in IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Langkawi, 2015. crossref

Microsoft, “Kinect for Windows Sensor Components and Specifications,” Microsoft, US, 2016.online

Hokuyo, “Scanning Laser Range Finder UTM-30LX/LN Specification,” Hokuyo Automatic CO., LTD, 2012.online

RoboPeak Team, “RPLIDAR Low Cost 360 degree 2D Laser Scanner (LIDAR) System,” 2014.online

J. Adler dan I. Parmryd, “Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient,” Cytometry Part A, vol. 77A, no. 8, pp. 733-742, 2010.crossref


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