3D Soil Compaction Mapping through Kriging-based Exploration with a Mobile Robot
This work addresses soil mapping for farms to enhance yields and reduce inputs, but it is incremental as it builds on existing Kriging methods with robotic adaptation.
The paper tackles the problem of laborious and costly manual soil mapping by proposing an automated method using a mobile robot to collect soil compaction data and build maps online, showing that using Kriging variance for exploration leads to more efficient data collection and better models.
This paper presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructed offline using Kriging, a form of Gaussian process regression. This process is laborious and costly, limiting the quality and resolution of the resulting information. Instead, we propose to use an outdoor mobile robot for automatic collection of soil condition data, building soil maps online and also adapting the robot's exploration strategy on-the-fly based on the current quality of the map. We show how using Kriging variance as a reward function for robotic exploration allows for both more efficient data collection and better soil models. This work presents the theoretical foundations for our proposal and an experimental comparison of exploration strategies using soil compaction data from a field generated with a mobile robot.