Advances in Embedded Computer Vision by Branislav Kisačanin, Margrit Gelautz

By Branislav Kisačanin, Margrit Gelautz

This illuminating assortment bargains a clean examine the very newest advances within the box of embedded laptop imaginative and prescient. rising parts coated via this complete text/reference contain the embedded consciousness of 3D imaginative and prescient applied sciences for various purposes, akin to stereo cameras on cellular units. fresh tendencies in the direction of the improvement of small unmanned aerial cars (UAVs) with embedded photograph and video processing algorithms also are tested. issues and lines: discusses intimately 3 significant good fortune tales – the improvement of the optical mouse, imaginative and prescient for patron robotics, and imaginative and prescient for automobile defense; studies state of the art study on embedded 3D imaginative and prescient, UAVs, automobile imaginative and prescient, cellular imaginative and prescient apps, and augmented fact; examines the possibility of embedded computing device imaginative and prescient in such state-of-the-art components because the web of items, the mining of huge facts streams, and in computational sensing; describes old successes, present implementations, and destiny challenges.

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The ground truth trajectories are shown in Fig. 6. 0 3,896 19 × 10 140 2 Consumer Robotics: A Platform for Embedding Computer Vision . . 39 Fig. 5 Example images from the Seq2 (left) and Seq3 (right). 1 Metrics We measure both the accuracy of the incrementally estimated trajectory and of the final view map. The view map is the set of poses of view nodes in the graph at the end of the run, including incremental optimization but without any post-processing. Comparing the trajectory to the reference reflects localization accuracy during the run.

We evaluate performance on sequences with ground truth and also compare to a standard graph-SLAM approach. 1 Introduction Over the past decade, computer vision algorithms have transitioned from the lab to the marketplace. Improvements in processors, memory density, and image sensor technology enable the deployment of sophisticated algorithms. E. Munich (B) · P. Fong · J. com P. com J. com E. com © Springer International Publishing Switzerland 2014 B. Kisaˇcanin and M. E. Munich et al. of smartphones and tablets has accelerated the pace of this trend.

While existing graph-based SLAM methods employ incremental graph optimizers to allow online operation, the number of poses in the graph continues to grow with time. One technique suggested for bounding this growth is that the robot be occasionally virtually “kidnapped,” disconnecting its current pose in the graph from previous poses and re-inserting it in using only recent observations [8]. This assumes both that the recent observations are sufficiently accurate to allow relocalization, and that the effective uncertainty of these observations is zero.

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