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Cited by 1,074

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Open access
·2019
DOI 10.1109/iros40897.2019
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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.

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Metadata

Publisher
IEEE
Published
2019
DOI
10.1109/iros40897.2019
OpenAlex
W2927638312
Cited by
1,074
References
36
Type
paratext
Language
en
Open access
green

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CoChat. (2026). Citation check: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://app.cochat.ai/papers/10.1109/iros40897.2019
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (2019). https://doi.org/10.1109/iros40897.2019

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