885 research outputs found
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
From Monocular SLAM to Autonomous Drone Exploration
Micro aerial vehicles (MAVs) are strongly limited in their payload and power
capacity. In order to implement autonomous navigation, algorithms are therefore
desirable that use sensory equipment that is as small, low-weight, and
low-power consuming as possible. In this paper, we propose a method for
autonomous MAV navigation and exploration using a low-cost consumer-grade
quadrocopter equipped with a monocular camera. Our vision-based navigation
system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense
reconstruction of the environment in real-time. Since LSD-SLAM only determines
depth at high gradient pixels, texture-less areas are not directly observed so
that previous exploration methods that assume dense map information cannot
directly be applied. We propose an obstacle mapping and exploration approach
that takes the properties of our semi-dense monocular SLAM system into account.
In experiments, we demonstrate our vision-based autonomous navigation and
exploration system with a Parrot Bebop MAV
The simultaneous localization and mapping (SLAM):An overview
Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality
- …