Introducing PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning

Khen Elimelech, Vadim Indelman

Abstract: Belief Space Planning (BSP) is a fundamental technique in artificial intelligence and robotics, which is widely used in the solution of problems such as online autonomous navigation and manipulation. Unfortunately, BSP is computationally demanding, especially when dealing with high-dimensional state spaces. We thus introduce PIVOT: Predictive Incremental Variable Ordering Tactic, a novel approach to improve planning efficiency. Although variable ordering has been extensively used for the state inference problem, variable ordering specifically for planning has hardly been considered. Interestingly, this tactic can also lead to improved loop-closing efficiency during state inference. We use the approach in an active-SLAM scenario, and demonstrate a significant improvement in efficiency. This approach follows our previous work regarding efficient BSP via belief sparsification.