Experimental comparison of open source vision based state estimation algorithms

Abstract The problem of state estimation using primarily visual data has received a lot of attention in the last decade. Several open source packages have appeared addressing the problem, each supported by impressive demonstrations. Applying any of these packages on a new dataset however, has been proven extremely challenging. Suboptimal performance, loss of localization, and challenges in customization have not produced a clear winner. Several other research groups have presented superb performance without releasing the code, sometimes materializing as commercial products. In this paper, ten of the most promising open source packages are evaluated, by cross validating them on the datasets provided for each package and by testing them on eight different datasets collected over the years in our laboratory. Indoor and outdoor, terrestrial and flying vehicles, in addition to underwater robots, cameras, and buoys were used to collect data. An analysis on the motions required for the different approaches and an evaluation of their performance is presented.

@inproceedings{QuaCos+16,
  author = { Alberto Quattrini Li and Adem Coskun and Sean M. Doherty
            and Shervin Ghasemlou and Apoorv S. Jagtap and
            MD Modasshir and Sharmin Rahman and Akanksha
            Singh and Marios Xanthidis and Jason M. O'Kane
            and Ioannis Rekleitis},
  booktitle = {Proc. International Symposium on Experimental Robotics},
  title = {Experimental comparison of open source vision based state
           estimation algorithms},
  year = {2016}
}