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An adaptive appearance-based map for long-term topological localization of mobile robots. Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract— This work considers a mobile service robot(More)
Fig. 1: We present a novel place recognition system that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image. The proposed system utilizes convolutional network features as robust landmark descriptors to recognize places despite severe viewpoint and condition changes, without requiring any environment-specific(More)
Real-world environments such as houses and offices change over time, meaning that a mobile robot's map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store(More)
— After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic(More)
(2013) Vision-only autonomous navigation using topometric maps. Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract— This paper presents a mapping and navigation system for a mobile(More)
— This paper introduces a minimalistic approach to produce a visual hybrid map of a mobile robot's working environment. The proposed system uses omnidirectional images along with odometry information to build an initial dense pose-graph map. Then a two level hybrid map is extracted from the dense graph. The hybrid map consists of global and local levels.(More)
(2014) Multiple map hypotheses for planning and navigating in non-stationary environments. Notice: Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published source: Abstract— This paper presents a method to enable a(More)
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has(More)
Evaluation of features for leaf classification in challenging conditions. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprint-ing/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution(More)