Informative Planning in the Presence of Outliers

TL;DR

This paper introduces a novel informative planning method that enables robots to revisit locations with outliers, improving environmental modeling accuracy despite sensing anomalies.

Contribution

It proposes a new Pareto MCTS-based objective allowing robots to re-visit outlier locations, reducing false filtering and enhancing model robustness.

Findings

Outperforms naive outlier filtering approaches

Improves environmental model accuracy in the presence of outliers

Enables effective outlier re-visitation strategy

Abstract

Informative planning seeks a sequence of actions that guide the robot to collect the most informative data to build a large-scale environmental model or learn a dynamical system. Existing work in informative planning mainly focuses on proposing new planners and applying them to various robotic applications such as environmental monitoring, autonomous exploration, and system identification. The informative planners optimize an objective given by a probabilistic model, e.g., Gaussian process regression (GPR). In practice, the ubiquitous sensing outliers can easily affect the model, resulting in a misleading objective. A straightforward solution is to filter out the outliers in the sensing data stream using an off-the-shelf outlier detector. However, informative samples are also scarce by definition so they might be falsely filtered out. In this paper, we propose a method to enable the robot to re-visit the locations where outliers were sampled besides optimizing the informative planning objective. The robot can collect more samples in the vicinity of outliers and update the outlier detector to reduce the number of false alarms. We achieve this by designing a new objective for the Pareto Monte Carlo tree search (MCTS). We demonstrate that the proposed framework performs better than applying an outlier detector naively.