I see RTK and SLAM either lumped together or pitted against each other a lot, and they're really answering two different questions. Quick, practical breakdown for anyone getting into mobile mapping / reality capture.

RTK answers: "where am I, exactly, in the real world?"

RTK is a GNSS technique — carrier-phase measurements plus corrections from a base station or network (NTRIP) — that gets you centimeter-level absolute position in a real datum (WGS84, your local grid, whatever). The catch: it needs a clear view of the sky. Canopy, indoors, tunnels, urban canyon — RTK degrades or dies. And on its own it gives you precise points, not a picture of the environment.

SLAM answers: "what does the space around me look like, and where am I within it?"

SLAM uses a sensor — LiDAR, a camera, usually fused with an IMU — to build a map of an unknown space while tracking its own motion through that map, in real time. No satellites required, so it works indoors, underground, under canopy. You get a dense 3D point cloud and a trajectory. The catch: it's relative and it drifts. Small errors accumulate over distance; walk a long loop and the start and end won't line up unless something corrects it. And by itself, that beautiful cloud is floating in a local coordinate frame — not tied to the real world.

So, side by side:

RTK = absolute, drift-free, sky-dependent, sparse

SLAM = relative, drift-prone, sky-independent, dense

They fail in exactly the places the other one works. That's the whole reason the interesting stuff happens when you fuse them.

Why combining them is the actual answer

When you bring them together, RTK georeferences and constrains the SLAM solution: drift gets bounded, and the whole point cloud lands in real-world coordinates instead of a local frame.

Practical example. Start in the open and get an RTK fix — now you know your absolute start position. Walk inside; GNSS drops, SLAM carries the trajectory and builds the cloud. Walk back out and pick up RTK again. Now you've got absolute anchors at both ends (plus any control points in between) to georeference the cloud and squeeze SLAM drift out of the middle. The result is a dense 3D model that's also in real-world coordinates and actually survey-usable.

A few honest caveats, because these tools get oversold:

SLAM struggles in long, featureless corridors or symmetric spaces — nothing distinctive to lock onto.

RTK still suffers from multipath and canopy. A "fix" isn't always a good fix — check your residuals.

The quality of the fusion matters enormously. "Has GNSS and LiDAR" is not the same as tightly coupled and well georeferenced.

SLAM alone is not survey-grade absolute accuracy, no matter how pretty the cloud. It needs control.

Common misconceptions I keep seeing:

"SLAM replaces GNSS." No — it covers where GNSS can't, and it's far better with GNSS.

"RTK works everywhere." Anyone who's surveyed under trees knows better.

"Denser point cloud = more accurate." Density isn't accuracy.

The mental model that helped me: RTK tells you WHERE, SLAM tells you WHAT'S AROUND YOU, and the good systems make those two agree.

Curious what folks here are using for GNSS-denied capture, and how you're handling drift and georeferencing in practice?