Hi everyone,
I’m a final-year student building an autonomous tracked robot for reconnaissance in collapsed-structure (rubble) environments. It enters confined voids, builds a map, and searches for signs of life before human responders enter. The robot moves slowly and uses a stop-and-scan routine: it stops, scans its surroundings, then advances.
⚙️ Current Hardware Stack:
- Chassis: Metal tracked chassis with suspension (12V encoder motors)
- Compute: Raspberry Pi 5 (8GB) for perception/SLAM + ESP32 for real-time motor control
- Sensors: RPLIDAR A1 (2D), Camera on a pan-tilt arm (360° bus servo for pan)
- Constraints: Battery-powered, limited budget, ~6 months to build and test
⚠️ My Concern:
The RPLIDAR A1 only scans a single horizontal plane. In rubble, the robot will pitch and roll, tilting the scan plane and missing overhangs, obstacles above/below the plane, or holes in the floor. I need 3D awareness to avoid unsafe navigation, but a true 3D LiDAR is far beyond my budget. I’m unsure whether a depth camera (e.g., RealSense D435i or OAK-D Lite) is the right alternative.
❓ Questions for the Community:
- RGB-D Camera for Mapping: Can an RGB-D camera completely replace the LiDAR for mapping here, or should it only supplement it? What is your experience with visual SLAM on vibrating tracked platforms, especially in dark, dusty, or textureless voids?
- 3D LiDAR vs Compromise: For a student prototype, is a true 3D LiDAR ever justified, or is a 2D LiDAR / depth camera combo the standard sensible compromise?
- Pi 5 Performance: Can a Raspberry Pi 5 realistically run RTAB-Map (RGB-D) alongside object detection? How do you manage the CPU load (reduced resolution/FPS, hardware acceleration, cooling, etc.)?
Any advice, repo links, or past experience reports would be hugely appreciated. Thank you!