VPS vs SLAM: Why You Almost Certainly Need Both

SLAM tracks in a frame that starts wherever the device started, so it drifts and stays private to the session. A visual positioning system returns a pose in the frame of a map that already exists, so the error doesn't build up and anything on the same map can read it.

Because each estimate is built on the one before it, so small errors compound. Any system that integrates motion without observing a known external reference drifts without bound. Loop closure corrects it when the device revisits somewhere it has already been, which does not help on a long one-way traverse. An absolute fix resets the error rather than slowing its growth.

When the space has never been captured, when one device works alone, and when nothing has to persist beyond the session. In an unmapped environment it is the only option available, and it is cheaper because there is no capture step. The moment a second device, a work order or tomorrow's session needs to read the position, you need a shared frame.

Visual SLAM uses camera images, which is cheap and depends on visible texture, so it degrades in the dark or against blank walls. LiDAR SLAM uses laser ranging, which is more geometrically precise and indifferent to lighting, at the cost of a sensor on every device. Both are relative, so fusing them produces a better relative estimate and still not an absolute one.

Not every frame, which is the common misunderstanding. Query on a timer, on entering a new area, or whenever tracking confidence drops. Between queries the on-device tracker carries the motion. How often depends on how fast drift accumulates on your hardware and how much error the application tolerates.

The absolute pose is published into the transform tree, so it arrives as a standard frame alongside odometry and the existing SLAM output rather than as a parallel system somebody has to reconcile. Every response carries a confidence value, and the integration should gate on it rather than trusting every pose.