34,983 research outputs found
An Experimental Distributed Framework for Distributed Simultaneous Localization and Mapping
Simultaneous Localization and Mapping (SLAM) is widely used in applications such as rescue, navigation, semantic mapping, augmented reality and home entertainment applications. Most of these applications would do better if multiple devices are used in a distributed setting. The distributed SLAM research would benefit if there is a framework where the complexities of network communication is already handled. In this paper we introduce such framework utilizing open source Robot Operating System (ROS) and VirtualBox virtualization software. Furthermore, we describe a way to measure communication statistics of the distributed SLAM system
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
A distributed optimization framework for localization and formation control: applications to vision-based measurements
Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures
PI-BA Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization
Bundle adjustment (BA) is a fundamental optimization technique used in many
crucial applications, including 3D scene reconstruction, robotic localization,
camera calibration, autonomous driving, space exploration, street view map
generation etc. Essentially, BA is a joint non-linear optimization problem, and
one which can consume a significant amount of time and power, especially for
large optimization problems. Previous approaches of optimizing BA performance
heavily rely on parallel processing or distributed computing, which trade
higher power consumption for higher performance. In this paper we propose
{\pi}-BA, the first hardware-software co-designed BA engine on an embedded
FPGA-SoC that exploits custom hardware for higher performance and power
efficiency. Specifically, based on our key observation that not all points
appear on all images in a BA problem, we designed and implemented a
Co-Observation Optimization technique to accelerate BA operations with
optimized usage of memory and computation resources. Experimental results
confirm that {\pi}-BA outperforms the existing software implementations in
terms of performance and power consumption.Comment: in Proceedings of IEEE FCCM 201
AUV SLAM and experiments using a mechanical scanning forward-looking sonar
Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
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