You can use RTAB-Map on a myAGV Jetson Nano only after matching its software to the robot’s actual image, ROS distribution, and sensor topics. The available documentation does not establish a tested, one-command RTAB-Map setup for the myAGV Jetson Nano 2023. First identify the installed software and confirm that the robot publishes usable sensor data and transforms; then choose a compatible RTAB-Map package or build path.
What RTAB-Map can do on this robot
RTAB-Map’s ROS wrapper connects ROS sensor and odometry data to a graph-based SLAM library with appearance-based loop closure. It supports RGB-D, stereo, and LiDAR inputs and can produce outputs including occupancy grids, point clouds, and OctoMaps. The ROS package separates SLAM, odometry, synchronization, and utility nodes, and it can use external odometry.
Elephant Robotics identifies the myAGV Jetson Nano 2023 as using an NVIDIA Jetson Nano B01 and customized Ubuntu Mate 20.04. Its specification lists a 360-degree laser radar with a stated scanning range of 0.12–8 m, an 8-megapixel camera with a 77-degree field of view and 2.96 mm focal length, and a maximum movement speed of 0.9 m/s. These are manufacturer specifications, not measurements of RTAB-Map accuracy, mapping speed, or usable range in a particular environment.
The camera specification alone does not establish that the camera provides depth. Before selecting an RGB-D or stereo setup, inspect the messages and calibration actually published by the installed camera driver.
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Identify the software installed on your Nano
Do not choose installation instructions based solely on the product page’s Ubuntu description. It does not name the ROS distribution installed on every unit, and an individual robot may have a different or updated image. Record the software versions and architecture while logged into the robot:
cat /etc/os-releaseshows the installed operating-system release.printenv ROS_DISTROreports the active ROS distribution when the ROS environment is sourced. If it prints nothing, that does not by itself prove ROS is absent; check the robot’s documented environment and installed packages.uname -mreports the processor architecture.- Record the JetPack, OpenCV, and RTAB-Map versions using the tools or package manager available on that image. Do not assume a version from another myAGV model applies.
Keep these results together. They determine which ROS branch and dependencies can be used and help distinguish a package problem from an image or driver mismatch.
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Choose a ROS-compatible RTAB-Map path
The current rtabmap_ros documentation says its ROS 2 wrapper requires ROS 2 Humble or newer and lists current ROS 2/Ubuntu pairings, including Humble with Ubuntu 22.04 and Jazzy or Kilted with Ubuntu 24.04. It also marks ROS 1 Noetic as end-of-life and pairs it with Ubuntu 20.04. That does not establish that a ROS 2 Humble binary package is appropriate for the Nano’s customized Ubuntu Mate 20.04 image, or that Noetic is installed on your robot.
| What you find on the robot | Next step |
|---|---|
| A ROS 1 distribution | Use RTAB-Map instructions and dependencies for that ROS 1 distribution and operating-system combination. Account for Noetic’s end-of-life status if that is the installed distribution. |
| ROS 2 Humble or newer | Check that the installed Ubuntu release, CPU architecture, sensor drivers, and available RTAB-Map packages match the documented ROS 2 combination before installing or building. |
| No active ROS environment, or an unclear/mixed installation | Resolve the robot image and ROS setup first. Installing a wrapper against an incompatible ROS environment is unlikely to fix the underlying mismatch. |
The ROS package index’s Jetson notes add an OpenCV caveat: if building to use OpenCV 4 Tegra, rebuild the vision_opencv stack to avoid conflicts with ROS binaries linked against a different, non-optimized OpenCV. The page’s detailed example is legacy Kinetic-era guidance, not a current Nano installation recipe. Check the supported versions for your actual image before applying any build steps, and avoid mixing binaries and locally built libraries that depend on conflicting OpenCV versions.
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Check sensor topics and transforms before starting SLAM
RTAB-Map cannot use a sensor merely because it is listed in the robot’s specifications. Confirm that the installed drivers publish messages consistently, that camera calibration is available when relevant, and that the transforms connect the sensor frames to the robot’s motion and base frames.
- Start the robot’s own supported sensor and base drivers using the instructions for the Nano image. Do not start an RTAB-Map launch file yet.
- List the active topics with
rostopic liston ROS 1 orros2 topic liston ROS 2. Identify the actual laser scan, camera, calibration, odometry, and transform topics rather than assuming names from another robot. - Inspect the message type and whether data is arriving. ROS 1 provides
rostopic typeandrostopic hz; ROS 2 providesros2 topic typeandros2 topic hz. Use them with the topic names reported by your system. - Check that camera calibration messages accompany image data when using camera-based input, and verify that timestamps are advancing and are usable together by the relevant nodes.
- Inspect the transform tree and confirm there is a connected path among the base, sensor, and odometry frames. Obtain the frame names from the running drivers and robot configuration; they are not specified for every Nano installation.
If a topic is missing, has the wrong message type, or is not connected by transforms, fix the driver or frame configuration before trying to tune SLAM. Starting RTAB-Map cannot compensate for absent or disconnected input data.
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Select the input mode from the data you actually have
- LiDAR: Use a LiDAR-oriented configuration only if the laser driver publishes usable scan data and the required motion/odometry and transforms are available.
- RGB-D: Choose this only when the camera system actually publishes synchronized color and depth data with calibration. The Nano product specification’s megapixel count does not prove that it does.
- Stereo: Use this only if the installed camera arrangement and driver provide calibrated stereo images and the synchronization expected by the chosen RTAB-Map configuration.
- Combined inputs: Combine modalities only after each stream works independently and the relevant timestamps, calibration, odometry, and transforms agree.
Decide which map representation the application needs—such as an occupancy grid for navigation or a point cloud for 3D inspection—rather than enabling every output by default. The available documentation does not establish a specific best configuration for this exact robot.
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Once the ROS environment, drivers, topics, and transforms are verified, use instructions matching the installed ROS distribution and the RTAB-Map branch selected for it. The package has separate node roles for odometry, synchronization, and SLAM; follow that branch’s configuration rather than copying launch names or frame settings from a different robot.
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- Start the Nano’s supported base and sensor drivers and verify their data is still arriving.
- Choose either the compatible RTAB-Map odometry path or an already functioning external odometry source. Confirm which odometry topic and frames the configuration expects.
- Configure synchronization and sensor inputs using the real topic names, message types, frame IDs, and calibration published by the Nano.
- Launch the relevant RTAB-Map nodes with a modest sensor workload. Watch for warnings about missing transforms, synchronization, calibration, or input data, and resolve those before increasing the workload.
- Move the robot conservatively in a simple environment and inspect whether the map and pose estimate update coherently. Stop if the estimate drifts, jumps, or fails to follow the robot; check the input streams and transforms before changing multiple SLAM settings at once.
No independently published benchmark cited here establishes mapping speed, memory use, localization accuracy, or a real-time frame rate for RTAB-Map on this exact robot. Monitor CPU and memory use and sensor message rates on the unit rather than assuming performance from the Jetson name or the manufacturer’s maximum movement-speed specification.
Keep model-specific tutorials separate
Elephant Robotics’ RTAB-Map tutorials linked here cover the myAGV Pro and myAGV Plus, not the myAGV Jetson Nano. Their workflows start model-specific bringup and camera drivers before SLAM; their launch packages, sensors, and ROS assumptions are not Nano instructions. For example, the Pro and Plus tutorials use different camera drivers. Treat the general sequence as a workflow illustration only, and use the Nano’s installed driver and package names.
Save mapping data deliberately
Decide how to preserve the mapping database before relying on a run. The cited database-save instructions belong to the Pro tutorial and do not establish the Nano launch behavior, save location, or whether saving occurs automatically. Check the documentation and configuration for the RTAB-Map version and launch setup installed on your Nano, then verify that a saved database can be found and reopened before treating a mapping session as backed up.
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