Object Detection and Spatial Coordinates Extraction Using a Monocular Camera for a Wheelchair Mounted Robotic Arm
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In the last decades, smart power wheelchairs have being used by people with motor skill impairment in order to improve their autonomy, independence and quality of life. The most recent power wheelchairs feature many technological devices, such as laser scanners to provide automatic obstacle detection or robotic arms to perform simple operations like pick and place. However, if a motor skill impaired user was able to control a very complex robotic arm, paradoxically he would not need it. For that reason, in this paper we present an autonomous control system based on Computer Vision algorithms which allows the user to interact with buttons or elevator panels via a robotic arm in a simple and easy way. Scale-Invariant Feature Transform (SIFT) algorithm has been used to detect and track buttons. Objects detected by SIFT are mapped in a tridimensional reference system collected with Parallel and Tracking Mapping (PTAM) algorithm. Real word coordinates are obtained using a Maximum-Likelihood estimator, fusing the PTAM coordinates with distance information provided by a proximity sensor. The visual servoing algorithm has been developed in Robotic Operative System (ROS) Environment, in which the previous algorithms are implemented as different nodes. Performances have been analyzed in a test scenario, obtaining good results on the real position of the selected objects.
KeywordsRobotic arm Power wheelchair Visual Servoing PBVS Eye-in-hand Computer Vision SIFT Features extraction PTAM ROS Human machine interface Assistive technology Open-source
This research was supported by Fondazione Cassa di Risparmio di Lucca in the framework of the project “RIMEDIO: Il braccio Robotico Intelligente per Migliorare l’autonomia delle pErsone con DIsabilità mOtoria”.
- 1.Madarasz, R.L., Heiny, L.C., Cromp, R.F., Mazur, N.M.: The design of an autonomous vehicle for the disabled. IEEE J. Robot. Autom. 2(3), 117–126 (1986)Google Scholar
- 2.Eftring, H., Boschian, K.: Technical results from manus user trials. In: International Conference on Rehabilitation Robotics, ICORR 1999 (1999)Google Scholar
- 3.Hillman, M., Gammie, A.: The bath institute of medical engineering assistive robot. In: Proceedings of ICORR, vol. 94, pp. 211–212 (1994)Google Scholar
- 4.Elarbi-Boudihir, M., Al-Shalfan, K.A.: Eye-in-hand, eye-to-hand configuration for a WMRA control based on visual servoing. In: 2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–6. IEEE (2013)Google Scholar
- 5.Palla, A., Frigerio, A., Sarti, L., Fanucci, L.: Embedded implementation of an eye-in-hand visual servoing control for a wheelchair mounted robotic arm. In: IEEE ICTS4eHealth (2016)Google Scholar
- 6.Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36 (2014)Google Scholar
- 7.Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan, November 2007Google Scholar
- 8.Engel, J., Sturm, J., Cremers, D.: Scale-aware navigation of a low-cost quadrocopter with a monocular camera. Robot. Autonom. Syst. 62 (2014)Google Scholar