论文标题
在现场大满贯：对挑战性动态农业环境的单眼映射和本地化的评估
SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment
论文作者
论文摘要
本文展示了一个能够将稀疏，间接，单眼的视觉大满贯的系统与离线和实时多视图立体声（MVS）重建算法相结合的系统。这种组合克服了在农业环境中使用的自动驾驶汽车或机器人遇到的许多障碍，例如过度重复的模式，需要非常详细的重建以及由不均匀的道路引起的突然动作。此外，使用单眼大满贯可以使我们的系统更易于与现有设备集成，因为我们不依赖LiDAR（昂贵且消耗的功能）或立体声摄像机（其校准对外部扰动敏感，例如摄像机的移位）。据我们所知，本文介绍了单眼大满贯的第一个评估结果，我们的工作通过模拟RGB-D SLAM来解决规模歧义，进一步探讨了无监督的深度估计，以解决规模的歧义，并显示我们的方法产生的重建对各种农业任务有用。此外，我们强调的是，我们的实验提供了有意义的见解，可以在农业环境下改善单眼大满贯系统。
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.
