AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment

Abstract

In this research, we proposed a stereo visual simultaneous localisation and mapping (SLAM) system that efficiently works in agricultural scenarios without compromising the performance and accuracy in contrast to the other state-of-the-art methods. The proposed system is equipped with an image enhancement technique for the ORB point and LSD line features recovery, which enables it to work in broader scenarios and gives extensive spatial information from the low-light and hazy agricultural environment. Firstly, the method has been tested on the standard dataset, i.e., KITTI and EuRoC, to validate the localisation accuracy by comparing it with the other state-of-the-art methods, namely VINS-SLAM, PL-SLAM, and ORB-SLAM2. The experimental results evidence that the proposed method obtains superior localisation and mapping accuracy than the other visual SLAM methods. Secondly, the proposed method is tested on the ROSARIO dataset, our low-light agricultural dataset, and O-HAZE dataset to validate the performance in agricultural environments. In such cases, while other methods fail to operate in such complex agricultural environments, our method successfully operates with high localisation and mapping accuracy.