Evaluation of Dead Reckoning Techniques in Underwater Environments
1. Lost in the Deep? Performance
Evaluation of Dead Reckoning
Techniques in Underwater
Environments
Marko Radeta, Claudio Rodrigues, Francisco Silva, Pedro Abreu, João
Pestana, Ngoc Thi Nguyen, Agustin Zuniga, Huber Flores, Petteri Nurmi
1
October 10, 2023, Cancun, Mexico
2. Importance
• Widely adopted in several navigation applications
2
Source: https://www.freepik.com/premium-vector/footprint-trail-human-
walking-route-footsteps-track-vector_17570436.htm
Source: https://www.freepik.com/free-vector/road-tire-tracks-white-
background_1107072.htm#query=tire%20tread&position=23&from_view
=search&track=ais
Cars
Source: https://www.vecteezy.com/png/27388481-speed-boat-
travel-floating-water-transport-route-path-way-in-ocean-blue-sea
Important characteristics include affordable, lightweight, and
energy-efficient
Surface vessels
Smartphones
5. Dead reckoning “underwater”
• Is the dead reckoning performance good underwater?
5
Underwater positioning (scuba, ROVs, AUVs)
6. Contributions
• Systematic evaluation: We benchmark
different (15) dead reckoning algorithms and
analyze their performance underwater
• Land vs underwater testbed: We design a
robust testbed that compares both conditions
• New insights: We present quantifiable
results about using dead reckoning
underwater
6
We can use it with 5% error for displacement and turn respectively –
some motion patterns underwater require specific algorithms
Source: DF Malan -
https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#/
media/File:Euler_AxisAngle.png
13. The experiments
Land
13
Drawn on the ground
We investigate the effects of distance on position
estimation at different distance magnitudes
Pre-defined geometrical shapes
16. Evaluation
Result: Average errors of displacement underwater lower are much
lower compared to the similar trajectory on the ground (warp).
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17. Evaluation
Result: Attitude estimation (Pitch, Roll and yaw) computed using
some algorithms (e.g., FAMC, Fourati) result in high errors on the
ground but lower errors when the experiments were performed in
the underwater environment.
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18. Evaluation
Result: Algorithm dependent performance for shape estimation
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(a) 4 m (b) 16m (c) 28 m – Lower displacement errors
(d) 4 m (e) 16 m (f) 28 m – Lower turn errors
19. Evaluation
Result: Warp estimation is difficult on the ground
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(a) (b) – Lower displacement and turn errors for spirals
(c) (d) – Lower displacement and turn errors for squares
20. Evaluation
Result: Warp estimations are easily captured underwater
20
(a) (b) – Lower displacement and turn errors
Thin rectangle indicates the base wooden surface structure
21. Summary and conclusions
• Systematic evaluation: We benchmark different (15) dead
reckoning algorithms and analyze their performance underwater
• Land vs underwater testbed: We design a robust testbed that
compares both conditions
• New insights:
• We can use it with 5% error for displacement and turn respectively
• Warp estimations are better captured underwater rather than on the
ground
• Performance of dead reckoning algorithm is quite volatile on the
ground
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22. Questions?
Thank you! (Do not hesitate to reach us via e-mail)
Marko Radeta (marko.radeta@wave-labs.org)
Claudio Rodrigues (claudio.rodrigues@wave-labs.org)
Francisco Silva (francisco.silva@wave-labs.org)
Pedro Abreu (pedro.abreu@wave-labs.org)
João Pestana (joao.pestana@wave-labs.org)
Ngoc Thi Nguyen (ngoc.nguyen@helsinki.fi)
Agustin Zuniga (agustin.zuniga@helsinki.fi)
Huber Flores (huber.flores@ut.ee)
Petteri Nurmi (petteri.nurmi@helsinki.fi)
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