Region Based SLAM-Aware Exploration: Efficient and Robust Autonomous Mapping Strategy That Can Scale

Autonomous exploration for mapping unknown large scale environments is a fundamental challenge in robotics, with efficiency in time, stability against map corruption and computational resources being crucial. This paper presents a novel approach to indoor exploration that addresses these key issues in existing methods. We introduce a Simultaneous Localization and Mapping (SLAM)-aware region-based exploration strategy that partitions the environment into discrete regions, allowing the robot to incrementally explore and stabilize each region before moving to the next one. This approach significantly reduces redundant exploration and improves overall efficiency. As the device finishes exploring a region and stabilizes it, we also perform SLAM keyframe marginalization, a technique which reduces problem complexity by eliminating variables, while preserving their essential information. To improves robustness and further enhance efficiency, we develop a check- point system that enables the robot to resume exploration from the last stable region in case of failures, eliminating the need for complete re-exploration. Our method, tested in real homes, office and simulations, outperforms state-of-the-art approaches. The improvements demonstrate substantial enhancements in various real world environments, with significant reductions in keyframe usage (85%), submap usage (50% office, 32% home), pose graph optimization time (78-80%), and exploration duration (10-15%). This region-based strategy with keyframe marginalization offers an efficient solution for autonomous robotic mapping.

POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality

In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty. Quantifying uncertainty in parameters of 3D-GS is necessary to understand the information gained from acquiring new images as in active perception, or identify redundant images which can be removed from memory due to resource constraints in online 3D-GS SLAM. We propose to quantify uncertainty and information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design, which is a classical solution to measuring information gain. By restructuring information quantification of 3D-GS through optimal experimental design, we arrive at multiple solutions, of which T-Optimality and D-Optimality perform the best quantitatively and qualitatively as measured on two popular datasets. Additionally, we propose a block diagonal approximation of the 3D-GS uncertainty, which provides a measure of correlation for computing more accurate information gain, at the expense of a greater computation cost.