I’m designing and building an outdoor autonomous robot for a school project. The main purpose of the robot is to clean a parking lot (about 30 × 70 meters) with wind and heavy rain conditions to consider. To do this, it needs to reliably detect and understand its environment — including parked cars and curbs.
Setup & context:
Robot type: seated brushing machine (goal: make it autonomous)
Dimensions: about 1.5 meters high
Platform: ROS2 Humble, Gazebo, Rviz2, etc.
Hardware: Nvidia Jetson Orin Nano Super Developer Kit
Budget: €500–€800, covered by the university (expandable with good justification)
Learning resources: following the Articulated Robotics YouTube series (but in ROS2 Humble instead of Foxy)
Sensor dilemma:
A single 2D LiDAR is not sufficient, since it would miss curbs and possibly lower cars due to the robot’s height.
Options I’m considering:
One 3D LiDAR
Or multiple depth “LiDAR” cameras around the robot, combined with a 2D LiDAR for global localization and mapping
What I’m looking for:
Sensor recommendations that work well in outdoor environments (rain/wind)
Suggestions for setups (single 3D LiDAR vs. multiple depth cameras + 2D LiDAR)
Must-have: ROS2 support
I’d really appreciate your ideas and sensor suggestions so I can make the right choice before integration.
The most robust and cost-effective approach for your constraints is likely multiple outdoor-rated 2D LiDARs, possibly supplemented by a small number of low-cost depth/single-point sensors, rather than a single 3D LiDAR.
Dedicated LiDARs are generally more robust in sun and rain than consumer-grade depth cameras, especially if you select IP-rated models. The tilted low LiDAR is highly effective for curb detection.
2D LiDARs are very well-supported in ROS2 for mapping (SLAM) and navigation (AMCL).
Potentially fits your budget with one mid-range and one entry-level unit.
Thank you very much for your insight and guidance!
Thanks to your advice, we were able to find a suitable LiDAR, and we’re currently using it successfully.
