Experimental assessment of plume mapping using point measurements from unmanned vehicles

This paper presents experiments to assess the plume mapping performance of autonomous robots. The paper compares several mapping algorithms including Gaussian Process regression, Neural networks and polynomial and piecewise linear interpolation. The methods are compared in Monte Carlo simulations using a well known plume model and in indoor experiments using a ground robot. Unlike previous work on mapping using unmanned vehicles, the indoor experiments were performed in a controlled and repeatable manner where a steady state ground truth could be obtained in order to properly assess the various regression methods using data from a real dispersive source and sensor. The effect of sampling time during data collection was assessed with regards to the mapping accuracy, and the data collected during the experiments have been made available. Overall, the Gaussian Process method was found to perform the best among the regression algorithms, showing more robustness to the noisy measurements obtained from short sampling periods, enabling an accurate map to be produced in significantly less time. Finally, plume mapping results are presented in uncontrolled outdoor conditions, using an unmanned aerial vehicle, to demonstrate the system in a realistic uncontrolled environment.