  Visual place recognition is an important component of systems for camera
localization and loop closure detection. It concerns the recognition of a
previously visited place based on visual cues only. Although it is a widely
studied problem for indoor and urban environments, the recent use of robots for
automation of agricultural and gardening tasks has created new problems, due to
the challenging appearance of garden-like environments. Garden scenes
predominantly contain green colors, as well as repetitive patterns and
textures. The lack of available data recorded in gardens and natural
environments makes the improvement of visual localization algorithms difficult.
In this paper we propose an extended version of the TB-Places data set, which
is designed for testing algorithms for visual place recognition. It contains
images with ground truth camera pose recorded in real gardens in different
seasons, with varying light conditions. We constructed and released a ground
truth for all possible pairs of images, indicating whether they depict the same
place or not. We present the results of a benchmark analysis of methods based
on convolutional neural networks for holistic image description and place
recognition. We train existing networks (i.e. ResNet, DenseNet and VGG NetVLAD)
as backbone of a two-way architecture with a contrastive loss function. The
results that we obtained demonstrate that learning garden-tailored
representations contribute to an improvement of performance, although the
generalization capabilities are limited.
