20.500.12556/DKUM-89278 Sensor fusion-based approach for the field robot localization on Rovitis 4.0 vineyard robot This study proposed an approach for robot localization using data from multiple low-cost sensors with two goals in mind, to produce accurate localization data and to keep the computation as simple as possible. The approach used data from wheel odometry, inertial-motion data from the Inertial Motion Unit (IMU), and a location fix from a Real-Time Kinematics Global Positioning System (RTK GPS). Each of the sensors is prone to errors in some situations, resulting in inaccurate localization. The odometry is affected by errors caused by slipping when turning the robot or putting it on slippery ground. The IMU produces drifts due to vibrations, and RTK GPS does not return to an accurate fix in (semi-) occluded areas. None of these sensors is accurate enough to produce a precise reading for a sound localization of the robot in an outdoor environment. To solve this challenge, sensor fusion was implemented on the robot to prevent possible localization errors. It worked by selecting the most accurate readings in a given moment to produce a precise pose estimation. To evaluate the approach, two different tests were performed, one with robot localization from the robot operating system (ROS) repository and the other with the presented Field Robot Localization. The first did not perform well, while the second did and was evaluated by comparing the location and orientation estimate with ground truth, captured by a hovering drone above the testing ground, which revealed an average error of 0.005 m±0.220 m in estimating the position, and 0.6°±3.5° when estimating orientation. The tests proved that the developed field robot localization is accurate and robust enough to be used on a ROVITIS 4.0 vineyard robot. localization odometry IMU RTK GPS vineyard robot sensors fusion ROS precision farming lokalizacija odometrija vinogradi roboti fuzija senzorjev precizno kmetovanje true false true Association of Overseas Chinese Agricultural, Biological and Food Engineers & CSAE Angleški jezik Slovenski jezik Znanstveno delo 2024-07-02 10:54:14 2024-07-02 11:27:49 2024-07-03 04:17:27 0000-00-00 00:00:00 2022 0 0 Str. 91-95 Št. 6 Letn. 15 November 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-01-06 2022-04-26 2022-11-01 634.8:004.9 137500931 10.25165/j.ijabe.20221506.6415 1934-6352 Rakun-2022-Sensor_fusion-based_approach_for_th.pdf Rakun-2022-Sensor_fusion-based_approach_for_th.pdf 1 FA9C724EB33C321064B24FC53ACB6862 f2a411159c42f624cf3767a6aae58cd1c606e8322ac6e51a54ab0a5ac20b9b3f be4953bb-3854-11ef-9e98-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=180990 https://www.ijabe.org/index.php/ijabe/article/view/6415 1 ab93ec34-3850-11ef-9e98-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=180988 Fakulteta za kmetijstvo in biosistemske vede 0 0 0