I'm curious, how do SLAM methods used by mobile robots or autonomous vehicles to recognize their environment actually work? What's the balance like between sensor fusion, map building, and localization steps? Especially, what are the differences between vision-based and LiDAR-based approaches?
How do SLAM algorithms work?
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SLAM algorithms are absolutely essential for mobile robots. While image-based approaches using cameras offer cheaper and more flexible data collection, LiDAR provides higher precision but comes at a higher cost. Which one have you had more experience with?
A few months ago, I was working on a simple robotics project involving a LiDAR-based SLAM algorithm. At first, noise from the sensor data was completely ruining the map—walls were coming out as shapeless blobs instead. Then I combined the LiDAR with camera images in sensor fusion, so the environment started being perceived in terms of both distance and texture, and suddenly the map became clear. Image-based methods, while richer in detail, struggled with lighting changes.
What fascinates me most about SLAM algorithms is that they require both mapping the environment and continuously updating the robot's own position. Comparing vision-based and LiDAR-based systems in terms of accuracy and computational load is really interesting! While LiDAR offers high precision, where do cameras still have an advantage?