SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment

TL;DR

This paper evaluates a monocular visual SLAM system combined with multi-view stereo reconstruction in challenging agricultural environments, demonstrating its effectiveness without relying on expensive sensors and providing insights for improvement.

Contribution

It presents the first evaluation of monocular SLAM in agricultural settings and explores unsupervised depth estimation to address scale ambiguity in this context.

Findings

Monocular SLAM combined with MVS improves reconstruction quality in agriculture.

Unsupervised depth estimation helps resolve scale ambiguity in monocular SLAM.

The system is easier to integrate due to lack of reliance on LiDAR or stereo cameras.

Abstract

This paper demonstrates a system capable of combining a sparse, indirect, monocular visual SLAM, with both offline and real-time Multi-View Stereo (MVS) reconstruction algorithms. This combination overcomes many obstacles encountered by autonomous vehicles or robots employed in agricultural environments, such as overly repetitive patterns, need for very detailed reconstructions, and abrupt movements caused by uneven roads. Furthermore, the use of a monocular SLAM makes our system much easier to integrate with an existing device, as we do not rely on a LiDAR (which is expensive and power consuming), or stereo camera (whose calibration is sensitive to external perturbation e.g. camera being displaced). To the best of our knowledge, this paper presents the first evaluation results for monocular SLAM, and our work further explores unsupervised depth estimation on this specific application scenario by simulating RGB-D SLAM to tackle the scale ambiguity, and shows our approach produces reconstructions that are helpful to various agricultural tasks. Moreover, we highlight that our experiments provide meaningful insight to improve monocular SLAM systems under agricultural settings.