Underwater Visual Multi-Modal 3D Sensing

In the underwater domain, optical sensors are extremely limited with respect to range, resolution, and accuracy in comparison to most terrestrial remote sensors. The reason for this is the medium water, which heavily interacts with electromagnetic signals and therefore reduces their corresponding signal-to-noise ratio. Also, many underwater areas can only be visited by remotely operated vehicles due to water pressure, turbidity, and or strong currents, posing a high risk for humans. This combination considerably increases the complexity of underwater metrology, and many applications currently require highly skilled personnel and large support vessels. Here, a simplification of these applications is presently effectively prevented by the performance gap of underwater optical systems in comparison to their terrestrial counterparts.

Motivated by the above limitations, this research work evaluates different optical sensing modalities when applied to the underwater domain and identifies their possible sweet spots. Based on these considerations, several novel fusion strategies for passive-active optical systems are presented able to reconstruct whole underwater scenes with high accuracy without relying on additional navigation systems. For their evaluation, a novel self-referenced optical 3D underwater scanner is implemented and used for several test setups as well as real-world scenarios. The implementation also includes a novel method for in-air calibration of flat-port cameras and integration into bundle adjustment frameworks for visual pose estimation. Here, the evaluation demonstrates that passive-active optical systems outperform standard methods when underwater sensor motion is a critical design parameter. The most significant advantage of self-referenced optical 3D scanners is that they compensate sensor motion in the same sensor domain as 3D measurements take place. This reduces the complexity of sensor co-calibration, ensures a similar accuracy for sensor pose and scene depth estimation, and broadens their possible application to smaller sensor platforms.