wkentaro/label_octomap

label_octomap is a research project from a 2017 robotics conference that helps robots understand the 3D world around them well enough to pick up specific objects, even when those objects are tucked behind other things on a shelf. Imagine a robot reaching into a cluttered bin and needing to grab the item in the back, this software gives the robot a way to build a live, 3D mental map of what's in front of it, where each object is, and how confident it is about what it's seeing. At a high level, it works by combining 3D mapping with object recognition in real time. Instead of just building a generic 3D model of a shelf (like "there's a solid shape here"), it labels each part of that 3D map with what object it likely belongs to, probabilistically, meaning it tracks confidence levels as new sensor data comes in. So the robot doesn't just see a blob, it sees "this part is probably a bottle, that part is probably a box," and it updates that understanding continuously as it moves or gets a better view. The people who would use this are robotics researchers and engineers working on manipulation tasks, specifically scenarios where a robot arm needs to interact with multiple distinct objects in a shared space, like a warehouse shelf or a parts bin. The example given in the project is literally "pick the backward object in a shelf bin," which maps directly to challenges like the Amazon Robotics Challenge (a competition that drove a lot of this kind of work around that time). The project is built on ROS, a widely used robotics software framework, and was designed for a specific humanoid robot platform (HRP-2). The README doesn't go into much detail beyond installation and a single launch command, so it's clearly aimed at other researchers who already have a compatible robotics setup rather than casual users. There's a linked video and paper that likely show the system in action and explain the approach in more depth.