This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.
Keywords: SLAM; agricultural robotics; behavior cloning; deep neural networks.