Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity

Abstract

In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment.

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

“…In the articles [54][55][56][57][58][59][60], the authors conducted studies similar to ours. The Portuguese researchers D. Gomes et al proposed automatic shape and position detection using a two-dimensional (2D) industrial laser to extract three-dimensional (3D) data, where object motion adds a third dimension through the laser beam [54].…”

“…The authors claim that this combination provides the most accurate navigation and mapping, while moving away from the GNSS. At the same time, Brazilian researchers in [59] propose a combined navigation method based on data obtained from LiDAR, the GNSS and an RGB camera. The authors substantiated the effectiveness of various methods at different stages of route construction depending on the obstacles and the visibility of each type of signal.…”