Image-based particle filtering for navigation in a semi-structured agricultural environment

Autonomous navigation of field robots in an agricultural environment is a difficult task due to the inherent uncertainty in the environment. The drawback of existing systems is the lack of robustness to these uncertainties. In this study we propose a vision-based navigation method to address these problems. The focus is on navigation through a maize field in an outdoor environment where the robot has to navigate through a corridor formed by two plant rows, detect the end of the rows, navigate the headland and turn into another corridor under natural conditions. The method is based on a Particle Filter (PF) using a novel measurement model, where we construct a model image from the particle and compare it directly with the measurement image after elementary processing, such as down-sampling, excessive-green filtering and thresholding. The new measurement model does not extract features from the image and thus does not suffer from errors associated with the feature extraction process. We show how PF can be used for robust navigation of a robot in a semi-structured agricultural environment such as maize fields with inherent uncertainty. We demonstrate the robustness of the algorithm through experiments in several maize fields with different row patterns, varying plant sizes and diverse lighting conditions. To date we have logged over 5 km of successful test runs in which the robot navigates through the corridor without touching the plant stems, accurately detects the end of the rows and traverses the headland