Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
This addresses the challenge of multi-sensor 3D modeling for robotics applications like inspection and manipulation, offering an incremental improvement by extending next-best-view planning to handle multiple sensors with theoretical guarantees.
The paper tackles the problem of efficiently selecting viewpoints for multiple depth cameras to build 3D scene models, proposing a method that frames it as submodular maximization under a matroid constraint, achieving near-optimal solutions with a polynomial-time greedy algorithm validated in simulations with up to 8 sensors and real-world experiments using two robot arms.
3D scene models are useful in robotics for tasks such as path planning, object manipulation, and structural inspection. We consider the problem of creating a 3D model using depth images captured by a team of multiple robots. Each robot selects a viewpoint and captures a depth image from it, and the images are fused to update the scene model. The process is repeated until a scene model of desired quality is obtained. Next-best-view planning uses the current scene model to select the next viewpoints. The objective is to select viewpoints so that the images captured using them improve the quality of the scene model the most. In this paper, we address next-best-view planning for multiple depth cameras. We propose a utility function that scores sets of viewpoints and avoids overlap between multiple sensors. We show that multi-sensor next-best-view planning with this utility function is an instance of submodular maximization under a matroid constraint. This allows the planning problem to be solved by a polynomial-time greedy algorithm that yields a solution within a constant factor from the optimal. We evaluate the performance of our planning algorithm in simulated experiments with up to 8 sensors, and in real-world experiments using two robot arms equipped with depth cameras.