Skeleton-Based Orienteering for Level Set Estimation

In recent years, the use of unmanned vehicles for monitoring spatial environmental phenomena has gained increasing attention. Within this context, an interesting problem is level set estimation, where the goal is to identify regions of space where the analyzed phenomena (for example the PH value in a body of water) is above or below a given threshold level. Typically, in the literature this problem is approached with techniques which search for the most interesting sampling locations to collect the desired information (i.e., locations where we can gain the most information by sampling). However, the common assumption underlying this class of approaches is that acquiring a sample is expensive (e.g., in terms of consumed energy and time). In this paper, we take a different perspective on the same problem by considering the case where a mobile vehicle can continuously acquire measurements with a negligible cost, through high rate sampling sensors. In this scenario, it is crucial to reduce the path length that the mobile platform executes to collect the data. To address this issue, we propose a novel algorithm, called Skeleton-Based Orienteering for Level Set Estimation (SBOLSE). Our approach starts from the LSE formulation introduced in [10] and formulates the level set estimation problem as an orienteering problem. This allows one to determine informative locations while considering the length of the path. To combat the complexity associated with the orienteering approach, we propose a heuristic approach based on the concept of topological skeletonization. We evaluate our algorithm by comparing it with the state of the art approaches (i.e., LSE and LSE-batch) both on a real world dataset extracted from mobile platforms and on a synthetic dataset extracted from CO2 maps. Results show that our approach achieves a near optimal classification accuracy while significantly reducing the travel distance (up to 70% w.r.t LSE and 30% w.r.t. LSE-batch).