3D Map Navigation
Localize and plan on a point-cloud map built with 3D SLAM.
When a flat 2D grid isn't enough — think ramps, split levels, or anything where height matters — you navigate against the point-cloud map you captured with 3D SLAM instead. 3D map navigation localizes the robot against that .pcd and plans paths over it.
Two pieces come up together: ICP localization, which matches the live LiDAR against the saved point cloud to track where the robot is, and the PCT planner, which plans a route from there to a goal. One thing to design around: this localizes and plans only — it doesn't drive the robot. A separate motion controller consumes the planned path, so pair it accordingly.
In the portal
The OpenMind portal lets you load a 3D (point-cloud) map and set navigation goals against it from the machine's autonomy view, the same way you would for a 2D map.

Before you start
You have a map with a saved
.pcd— meaning it was saved while 3D SLAM was running.The robot type supports 3D (
slam_3d_supportedinGET /status).SLAM and Nav2 are both stopped.
Running it
Start it against a 3D map:
The planner listens for goal poses on its goal topic (/goal_pose by default) and publishes the path it finds. Send goals from your integration or the portal. When you're done:
Parameters
POST /start/nav3d
map_name
string
yes
—
A map with a saved .pcd
scene
string
no
Isaacsim
Tomography scene config
goal_topic
string
no
/goal_pose
Topic the planner listens on for goals
goal_layer
int
no
0
Tomogram layer index for the goal
If something goes wrong
400— SLAM or Nav2 is running, or 3D nav is already up. Stop the other one first.404— the map has no.pcd; it wasn't saved during 3D SLAM.400unsupported — the robot type has no 3D stack.
For system endpoints like GET /status and base control, see the Autonomy API Overview.
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