Place recognition can be achieved by identifying whether a pair of images (a labeled reference image and a query image) depict the same place, regardless of appearance changes due to different viewpoints or lighting conditions. It is an important component of systems for camera localization and for loop closure detection, and a widely studied problem for indoor or urban environments. Recently, the use of robots in agriculture and automatic gardening has created new challenges due to the highly repetitive appearance with prevalent green color and repetitive texture of garden-like scenes. The lack of available data recorded in gardens or plant fields makes difficult to improve localization algorithms for such environments. In this paper, we propose a new data set of garden images for testing algorithms for visual place recognition. It contains images with ground truth camera pose recorded in real gardens at different times, with varying light conditions. We also provide ground truth for all possible pairs of images, indicating whether they depict the same place or not. We also performed a thorough benchmark of several holistic (whole-image) descriptors and provide the results on the proposed data set. We observed that existing descriptors have difficulties with scenes with repetitive textures and large changes of camera viewpoint.

localization algorithms, data set, garden images, visual place recognition, ground truth camera, varying light conditions, whole-image, repetitive textures, camera viewpoint, TB-places, garden environments, reference image, query image, appearance changes, lighting conditions, camera localization, loop closure detection, urban environments, automatic gardening, highly repetitive appearance, prevalent green color, repetitive texture