Simultaneous localization and mapping based real-time inter-row tree tracking technique for unmanned aerial vehicle

This paper presents a review of previous work in the field of the unmanned autonomic vehicles in the agricultural field, particularly in the real-time inter-row tracking and localization techniques. A new method in row detection techniques based on simultaneous localization and mapping is proposed. The inter-row tracking technique differs from other available approaches done in terms of detection and navigation means. This technique is purposely for small-scaled unmanned aerial vehicle where substantial devices or equipments is unwelcome due to its enormous weight and size. Vision-based, laser-based and stereo vision-based techniques found to be an accurate row detection technique, however, proven to be impractical for on-board UAV detection and navigation implementation, as bulky computer as well as fast and robust processors is necessitated to perform all the massive computations and algorithmic tasks. In the way to maneuver the autonomic vehicles and robots to the admissible way points, the GPS receiver allows for precise in navigation and localization in the previous implementation. Unfortunately, it is impractical in applications where there are no GPS signals or receptions such as in the canopied environment like in the agriculture fields e.g. palm oil, rubber and papaya. Thus, the proposed new technique which applies an uncomplicated algorithmic sensory measurement at two visible landmarks, which are the trees, and uses them to predict the admissible way points for UAV navigation without carrying the burden cause by the substantial computers, monolithic computations and distortion of the GPS signals.