Hi all,
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.
Thanks in advance!