716 research outputs found
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
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
Multi-Session Visual SLAM for Illumination Invariant Localization in Indoor Environments
For robots navigating using only a camera, illumination changes in indoor
environments can cause localization failures during autonomous navigation. In
this paper, we present a multi-session visual SLAM approach to create a map
made of multiple variations of the same locations in different illumination
conditions. The multi-session map can then be used at any hour of the day for
improved localization capability. The approach presented is independent of the
visual features used, and this is demonstrated by comparing localization
performance between multi-session maps created using the RTAB-Map library with
SURF, SIFT, BRIEF, FREAK, BRISK, KAZE, DAISY and SuperPoint visual features.
The approach is tested on six mapping and six localization sessions recorded at
30 minutes intervals during sunset using a Google Tango phone in a real
apartment.Comment: 6 pages, 5 figure
Map Management Approach for SLAM in Large-Scale Indoor and Outdoor Areas
This work presents a semantic map management approach for various environments by triggering multiple maps with different simultaneous localization and mapping (SLAM) configurations. A modular map structure allows to add, modify or delete maps without influencing other maps of different areas. The hierarchy level of our algorithm is above the utilized SLAM method. Evaluating laser scan data (e.g. the detection of passing a doorway) triggers a new map, automatically choosing the appropriate SLAM configuration from a manually predefined list. Single independent maps are connected by link-points, which are located in an overlapping zone of both maps, enabling global navigation over several maps. Loop- closures between maps are detected by an appearance-based method, using feature matching and iterative closest point (ICP) registration between point clouds. Based on the arrangement of maps and link-points, a topological graph is extracted for navigation purpose and tracking the global robot's position over several maps. Our approach is evaluated by mapping a university campus with multiple indoor and outdoor areas and abstracting a metrical-topological graph. It is compared to a single map running with different SLAM configurations. Our approach enhances the overall map quality compared to the single map approaches by automatically choosing predefined SLAM configurations for different environmental setups
Visual Place Recognition: A Tutorial
Localization is an essential capability for mobile robots. A rapidly growing
field of research in this area is Visual Place Recognition (VPR), which is the
ability to recognize previously seen places in the world based solely on
images. This present work is the first tutorial paper on visual place
recognition. It unifies the terminology of VPR and complements prior research
in two important directions: 1) It provides a systematic introduction for
newcomers to the field, covering topics such as the formulation of the VPR
problem, a general-purpose algorithmic pipeline, an evaluation methodology for
VPR approaches, and the major challenges for VPR and how they may be addressed.
2) As a contribution for researchers acquainted with the VPR problem, it
examines the intricacies of different VPR problem types regarding input, data
processing, and output. The tutorial also discusses the subtleties behind the
evaluation of VPR algorithms, e.g., the evaluation of a VPR system that has to
find all matching database images per query, as opposed to just a single match.
Practical code examples in Python illustrate to prospective practitioners and
researchers how VPR is implemented and evaluated.Comment: IEEE Robotics & Automation Magazine (RAM
- …