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An efficient pose estimation for limited-resourced MAVs using sufficient statistics

- Senthooran, Ilankaikone, Barca, Jan Carlo, Kamruzzaman, Joarder, Murhsed, Manzur, Chung, Hoam

  • Authors: Senthooran, Ilankaikone , Barca, Jan Carlo , Kamruzzaman, Joarder , Murhsed, Manzur , Chung, Hoam
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015; Hamburg; Germany; 28th September-2nd October 2015 Vol. 2015, p. 3735-3740
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  • Reviewed:
  • Description: We present a computationally efficient RGB-D based pose estimation solution for less computationally resourced MAVs, which are ideally suited as members in a swarm. Our approach applies the sufficient statistics derived for a least-squares problem to our problem context. RANSAC-based outlier detection in aligning corresponding feature points is a time consuming operation in visual pose estimation. The additive nature of the used sufficient statistics significantly reduces the computation time of the RANSAC procedure since the pose estimation in each test loop can be computed by reusing previously computed sufficient statistics. This eliminates the need for recomputing estimates from scratch each time. A simpler hypotheses testing method gave similar performance in terms of speed but less accurate than our proposed method. We further increase the efficiency by reducing the problem size to four dimensions using attitude data from an Attitude and Heading Reference System (AHRS). Using a real-world dataset, we show that our algorithm saves up to 94% of computation time for the RANSAC-based procedure in pose estimation while improving the accuracy.

Search and tracking algorithms for swarms of robots: A survey

- Senanayake, Madhubhashi, Senthooran, Ilankaikaone, Barca, Jan Carlo, Chung, Hoam, Kamruzzaman, Joarder, Murshed, Manzur

  • Authors: Senanayake, Madhubhashi , Senthooran, Ilankaikaone , Barca, Jan Carlo , Chung, Hoam , Kamruzzaman, Joarder , Murshed, Manzur
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Robotics and Autonomous Systems Vol. 75, no. Part B (2016), p. 422-434
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  • Reviewed:
  • Description: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. © 2015 Elsevier B.V.
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