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Benini, A., Rutherford, M.J. and Valavanis, K.P. (2016) Real-Time, GPU-Based Pose Estimation of a UAV for Autonomous Takeoff and Landing. IEEE International Conference on Robotics and Automation, Stockholm, 16-21 May 2016, 3463-3470.
https://doi.org/10.1109/icra.2016.7487525

has been cited by the following article:

• TITLE: Acceleration of Points to Convex Region Correspondence Pose Estimation Algorithm on GPUs for Real-Time Applications

  AUTHORS: Raghu Raj P. Kumar, Suresh S. Muknahallipatna, John E. McInroy

  KEYWORDS: Pose Estimation, Parallel Computing, GPU, CUDA, Real Time Image Processing

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.17, December 29, 2016

  ABSTRACT: In our previous work, a novel algorithm to perform robust pose estimation was presented. The pose was estimated using points on the object to regions on image correspondence. The laboratory experiments conducted in the previous work showed that the accuracy of the estimated pose was over 99% for position and 84% for orientation estimations respectively. However, for larger objects, the algorithm requires a high number of points to achieve the same accuracy. The requirement of higher number of points makes the algorithm, computationally intensive resulting in the algorithm infeasible for real-time computer vision applications. In this paper, the algorithm is parallelized to run on NVIDIA GPUs. The results indicate that even for objects having more than 2000 points, the algorithm can estimate the pose in real time for each frame of high-resolution videos.