28,803 research outputs found
Visual Place Recognition for Autonomous Robots
Autonomous robotics has been the subject of great interest within the research community over the past few decades. Its applications are wide-spread, ranging from health-care to manufacturing, goods transportation to home deliveries, site-maintenance to construction, planetary explorations to rescue operations and many others, including but not limited to agriculture, defence, commerce, leisure and extreme environments. At the core of robot autonomy lies the problem of localisation, i.e, knowing where it is and within the robotics community, this problem is termed as place recognition. Place recognition using only visual input is termed as Visual Place Recognition (VPR) and refers to the ability of an autonomous system to recall a previously visited place using only visual input, under changing viewpoint, illumination and seasonal conditions, and given computational and storage constraints.
This thesis is a collection of 4 inter-linked, mutually-relevant but branching-out topics within VPR: 1) What makes a place/image worthy for VPR?, 2) How to define a state-of-the-art in VPR?, 3) Do VPR techniques designed for ground-based platforms extend to aerial platforms? and 4) Can a handcrafted VPR technique outperform deep-learning-based VPR techniques? Each of these questions is a dedicated, peer-reviewed chapter in this thesis and the author attempts to answer these questions to the best of his abilities.
The worthiness of a place essentially refers to the salience and distinctiveness of the content in the image of this place. This salience is modelled as a framework, namely memorable-maps, comprising of 3 conjoint criteria: a) Human-memorability of an image, 2) Staticity and 3) Information content. Because a large number of VPR techniques have been proposed over the past 10-15 years, and due to the variation of employed VPR datasets and metrics for evaluation, the correct state-of-the-art remains ambiguous. The author levels this playing field by deploying 10 contemporary techniques on a common platform and use the most challenging VPR datasets to provide a holistic performance comparison. This platform is then extended to aerial place recognition datasets to answer the 3rd question above. Finally, the author designs a novel, handcrafted, compute-efficient and training-free VPR technique that outperforms state-of-the-art VPR techniques on 5 different VPR datasets
Visual Place Recognition for Autonomous Mobile Robots
Horst M, Möller R. Visual Place Recognition for Autonomous Mobile Robots. Robotics. 2017;6(2): 9.Place recognition is an essential component of autonomous mobile robot navigation. It is used for loop-closure detection to maintain consistent maps, or to localize the robot along a route, or in kidnapped-robot situations. Camera sensors provide rich visual information for this task. We compare different approaches for visual place recognition: holistic methods (visual compass and warping), signature-based methods (using Fourier coefficients or feature descriptors (able for binary-appearance loop-closure evaluation, ABLE)), and feature-based methods (fast appearance-based mapping, FabMap). As new contributions we investigate whether warping, a successful visual homing method, is suitable for place recognition. In addition, we extend the well-known visual compass to use multiple scale planes, a concept also employed by warping. To achieve tolerance against changing illumination conditions, we examine the NSAD distance measure (normalized sum of absolute differences) on edge-filtered images. To reduce the impact of illumination changes on the distance values, we suggest to compute ratios of image distances to normalize these values to a common range. We test all methods on multiple indoor databases, as well as a small outdoor database, using images with constant or changing illumination conditions. ROC analysis (receiver-operator characteristics) and the metric distance between best-matching image pairs are used as evaluation measures. Most methods perform well under constant illumination conditions, but fail under changing illumination. The visual compass using the NSAD measure on edge-filtered images with multiple scale planes, while being slower than signature methods, performs best in the latter cas
Appearance-based localization for mobile robots using digital zoom and visual compass
This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally
CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning
Visual navigation tasks in real-world environments often require both
self-motion and place recognition feedback. While deep reinforcement learning
has shown success in solving these perception and decision-making problems in
an end-to-end manner, these algorithms require large amounts of experience to
learn navigation policies from high-dimensional data, which is generally
impractical for real robots due to sample complexity. In this paper, we address
these problems with two main contributions. We first leverage place recognition
and deep learning techniques combined with goal destination feedback to
generate compact, bimodal image representations that can then be used to
effectively learn control policies from a small amount of experience. Second,
we present an interactive framework, CityLearn, that enables for the first time
training and deployment of navigation algorithms across city-sized, realistic
environments with extreme visual appearance changes. CityLearn features more
than 10 benchmark datasets, often used in visual place recognition and
autonomous driving research, including over 100 recorded traversals across 60
cities around the world. We evaluate our approach on two CityLearn
environments, training our navigation policy on a single traversal. Results
show our method can be over 2 orders of magnitude faster than when using raw
images, and can also generalize across extreme visual changes including day to
night and summer to winter transitions.Comment: Preprint version of article accepted to ICRA 202
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Visual localization is an attractive problem that estimates the camera
localization from database images based on the query image. It is a crucial
task for various applications, such as autonomous vehicles, assistive
navigation and augmented reality. The challenging issues of the task lie in
various appearance variations between query and database images, including
illumination variations, dynamic object variations and viewpoint variations. In
order to tackle those challenges, Panoramic Annular Localizer into which
panoramic annular lens and robust deep image descriptors are incorporated is
proposed in this paper. The panoramic annular images captured by the single
camera are processed and fed into the NetVLAD network to form the active deep
descriptor, and sequential matching is utilized to generate the localization
result. The experiments carried on the public datasets and in the field
illustrate the validation of the proposed system.Comment: Accepted by ITSC 201
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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