Abstract – A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct methods and tightly-coupled optimization techniques that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

@inproceedings{JoshiIROS2019,
  author       = {Bharat Joshi and Sharmin Rahman and Michail Kalaitzakis
		 and Brennan Cain and James Johnson and Marios Xanthidis
		 and Nare Karapetyan and Alan Hernandez and Alberto
		 {Quattrini Li} and Nikolaos Vitzilaios and Ioannis
		 Rekleitis},
  title        = {Experimental Comparison of Open Source
		 Visual-Inertial-Based State Estimation Algorithms in the
		 Underwater Domain},
  booktitle    = {IEEE/RSJ International Conference on Intelligent Robots
		 and Systems (IROS)},
  year	       = {2019},
  pages        = {7221--7227},
}