Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter

Autonomous navigation of robots in an agricultural environment is a difficult task due to the inherent uncertainty in the environment. Many existing agricultural robots use computer vision and other sensors to supplement Global Positioning System (GPS) data when navigating. Vision based methods are sensitive to ambient lighting conditions. This is a major disadvantage in an outdoor environment. The current study presents a novel probabilistic sensor model for a 2D range finder (LIDAR) from first principles. Using this sensor model, a particle filter based navigation algorithm (PF) for autonomous navigation in a maize field was developed. The algorithm was tested in various field conditions with varying plant sizes, different row patterns and at several scanning frequencies. Results showed that the Root Mean Squared Error of the robot heading and lateral deviation were equal to 2.4 degrees and 0.04 m, respectively. It was concluded that the performance of the proposed navigation method is robust in a semi-structured agricultural environment.