MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping
This addresses the challenge of active mapping for robotics and autonomous systems by improving long-term planning, though it appears incremental as it builds on existing occupancy networks and tree-search methods.
The paper tackles the problem of inefficient exploration and incomplete reconstruction in active mapping by introducing MAGICIAN, a long-term planning framework that uses Imagined Gaussians for scene representation, achieving state-of-the-art performance across indoor and outdoor benchmarks.
Active mapping aims to determine how an agent should move to efficiently reconstruct an unknown environment. Most existing approaches rely on greedy next-best-view prediction, resulting in inefficient exploration and incomplete scene reconstruction. To address this limitation, we introduce MAGICIAN, a novel long-term planning framework that maximizes accumulated surface coverage gain through Imagined Gaussians, a scene representation derived from a pre-trained occupancy network with strong structural priors. This representation enables efficient computation of coverage gain for any novel viewpoint via fast volumetric rendering, allowing its integration into a tree-search algorithm for long-horizon planning. We update Imagined Gaussians and refine the planned trajectory in a closed-loop manner. Our method achieves state-of-the-art performance across indoor and outdoor benchmarks with varying action spaces, demonstrating the critical advantage of long-term planning in active mapping.