Seeing and localising under water, where most of what works on land stops working. There is no GPS below the surface, cameras see a few metres on a good day, and the link to a teammate or to shore carries kilobits per second.
Light
Light goes missing in two ways. Attenuation removes the direct signal from the object, red first, so colour depends on range. Backscatter adds light scattered back by particles, a veil that thickens with distance and eats contrast. Sea-thru gives the two effects separate range-dependent coefficients, and nearly every reconstruction paper since inherits its model. Then add turbidity that changes from one dive to the next, a low-texture seafloor, caustics in shallow water, fish and marine snow, and flat camera ports that bend rays so the pinhole model drifts towards the edges of the image (Refractive COLMAP). Moving cameras, stirred sediment and a blank water column are on Kaveti and others’ list of field failures.
Navigation
- Lean on the other sensors. TURTLMap and AQUA-SLAM trust the Doppler velocity log, IMU and pressure sensor when the camera has nothing to grip, and use the camera for the map.
- Fuse camera and sonar. Sonar gives range without elevation, a camera gives bearing without range. Opti-acoustic SLAM takes each from where it is good. In its ablation, matching on geometry alone made the trajectory worse than plain odometry (1.40 m against 0.58 m).
- Teams. DRACo-SLAM2 closes loops between sonar robots by exchanging compact summaries (1.35 kbit on average) and asking for raw scans only after a match, inside a 62.5 kbps modem. The links in its experiments are replayed from separate recordings, and all robots stay at one depth.
- Relocalising after years. The Eiffel Tower set (four visits over five years) and the benthic localisation set (five sites, up to six years) show place recognition is much weaker underwater than on land.
Reconstruction
Most underwater splatting papers (SeaThru-NeRF, WaterSplatting, SeaSplat, UW-GS, RUSplatting) model the water inside the renderer, and assume structure-from-motion poses from dense, clear views. The cross-regime study shows where that breaks: structure from motion registers 99.5% of frames in clear water and none at 12 NTU. Under a light that moves with the vehicle, a renderer that ignores the medium beats both medium-aware ones. Swimm3R and AquaFlow move the medium model upstream into pose estimation, and Wat3R adapts the geometry model itself on unlabelled dive video.
Image metrics reward pictures. Inspection asks geometric questions, and the cross-regime study finds the photometric leader last on an industrial survey’s geometry.
Depth models
MoGe, UniDepth, Depth Anything 3 and VGGT give dense geometry from one cheap camera, and Fast-FoundationStereo runs in 14 to 49 ms on a desktop GPU. Underwater they degrade badly until fine-tuned (Cai and Metzler). Scale from one image is a learned prior about object sizes, and the seafloor has few familiar objects. Pow3R-SLAM found that 3% of pixels with true depth is a better prompt than dense wrong depth, and turbid water produces dense, confident, wrong depth.
Questions
- How does a vehicle know its depth or pose estimate has gone wrong, when turbid water fools the confidence head as well?
- What is worth sending over an acoustic modem? MERLION picks frames for a thin link; nobody yet picks measurements for a team.
- How do you get ground truth in murky water, when the reference tool (structure from motion) is the first thing to fail?
Related: collaborative SLAM, robust estimation, foundation models for robotics, Singapore scenarios, multi-robot multimodal perception.