What is Mobile Robot Navigation Data?
Mobile Robot Navigation Data — sensor logs of a robot moving through space, with the poses, maps and trajectories needed to train or evaluate navigation.
The core streams are lidar scans, wheel odometry, IMU readings and camera frames, each with its own timestamp. Rates are modest next to manipulation: a 2D lidar typically scans at 5 to 15 Hz and a 3D lidar at 10 to 20 Hz, while cameras run at 30. What a navigation stack actually consumes is usually an occupancy grid or costmap built from those scans, not the raw data.
Recording is straightforward — a ROS bag on the robot, driving the route — which is exactly why the hard part is route design. Two corridors that look identical to a lidar are the classic failure: scan matching aliases and the robot localizes itself in the wrong aisle. Glass walls and mirrors return nothing, wheel slip drifts odometry, and a robot that is lifted and set down elsewhere loses its pose entirely.
Most navigation policies are trained in simulation first, on scene sets such as Habitat-Matterport 3D, which contains 1,000 building-scale reconstructions, then evaluated on real runs. Capture for a specific building, sensor rig or failure case can be commissioned to order; the specification is the floor plan, the sensor and the conditions the robot has to survive.
Related terms
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Sim-to-Real Transfer
Sim-to-Real Transfer — training a policy in simulation and getting it to work on physical hardware, across the gap between the two.
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Warehouse Robotics Data
Warehouse Robotics Data — sensor and telemetry data from robots working in fulfillment centers: picking, moving totes and building pallets.
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Embodied AI Data
Embodied AI Data — training data for systems that act in the physical world, where every sample records what was perceived, what was done and what happened.
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