The 2.3m Drift That Broke My Demo
I ran a Turtlebot4 through a 50m loop in our office hallway using ROS2 Nav2's default SLAM Toolbox setup. When it returned to the start, the map showed 2.3 meters of drift. Same hardware, same path, but with Isaac ROS's cuVSLAM backend — drift dropped to 0.4 meters.
This isn't about GPU acceleration making things faster. It's about fundamentally different approaches to visual-inertial odometry that produce maps you can actually navigate on. If you're building AMRs for warehouses or hospitals, that difference matters the moment you try to loop-close across a 200m facility.
What Actually Changed Between Setups
ROS2 Nav2 ships with slam_toolbox by default, which uses 2D laser scan matching. Most tutorials pair it with depthimage_to_laserscan to fake a lidar from an RGB-D camera. The math is straightforward: project depth points onto a horizontal plane, run Karto SLAM's scan matching.
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