Place recognition in gardens by learning visual representations: data set and benchmark analysis
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Camera LocalizationImage DescriptionLoop Closure DetectionVisual LocalizationVisual Place RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
EchoVPR: Echo State Networks for Visual Place Recognition
Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image…
Autonomous NavigationVisual Place RecognitionFilter Early, Match Late: Improving Network-Based Visual Place Recognition
CNNs have excelled at performing place recognition over time, particularly when the neural network is optimized for localization in the current environmental conditions. In this paper we investigate the concept of featur…
Visual Place RecognitionDeep-Plant: Plant Identification with convolutional neural networks
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen f…
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image. Despite years of wisdom …
image-classificationImage ClassificationImage DescriptionImage Retrieval+4GardenDesigner: Encoding Aesthetic Principles into Jiangnan Garden Construction via a Chain of Agents
Jiangnan gardens, a prominent style of Chinese classical gardens, hold great potential as digital assets for film and game production and digital tourism. However, manual modeling of Jiangnan gardens heavily relies on ex…