1,164 research outputs found
CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey
Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated 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
Eksperimentalna usporedba metoda izgradnje mrežastih karata prostora koriÅ”tenjem ultrazvuÄnih senzora
For successful usage of mobile robots in human working areas several navigation problems have to be solved. One of the navigational problems is the creation and update of the model or map of a mobile robot working environment. This article describes most used types of the occupancy grid maps based sonar range readings. These maps are: (i) Bayesian map, (ii) Dempster-Shafer map, (iii) Fuzzy map, (iv) Borenstein map, (v) MURIEL map, and (vi) TBF map. Besides the maps description, a memory consumption and computation time comparison is done. Simulation validation is done using the AMORsim mobile robot simulator for Matlab and experimental validation is done using a Pioneer 3DX mobile robot. Obtained results are presented and compared regarding resulting map quality.Za uspjeÅ”nu primjenu mobilnih robota u radnim prostorima s ljudima potrebno je rijeÅ”iti razliÄite probleme navigacije. Jedan od problema navigacije jest kreiranje modela i ukljuÄivanje novih informacija o radnoj okolini mobilnog robota u model radne okoline ili kartu. Älanak opisuje Äesto koriÅ”tene tipove mrežastih karata prostora zasnovanih na oÄitanjima ultrazvuÄnih osjetila udaljenosti. ObraÄeni modeli prostora su: (i) Bayesova karta, (ii) Dempster-Shaferova karta, (iii) neizrazita karta, (iv) Borensteinova karta, (v) MURIEL karta i (vi) TBF karta. Osim opisa, u Älanku je dana i usporedba implementiranih algoritama prema memorijskim i raÄunskim zahtjevima. Simulacijska provjera napravljena je koriÅ”tenjem AMORsim simulatora mobilnog robota za programski paket Matlab, a eksperimentalna provjera napravljena je koriÅ”tenjem Pioneer 3DX mobilnog robota. TakoÄer su prikazani dobiveni rezultati uz usporedbu njihove kakvoÄe
Robot Localization Using Visual Image Mapping
One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to be able to determine its location. The calculation of the robot\u27s pose is an optimization problem making use of the robot\u27s internal navigation sensors and data fusion of range sensor readings to find the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use to avoid obstacles, communicate with other agents in the same environment, and locate targets. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera\u27s field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extract distance information to each obstacle. This range data is then used in conjunction with mapping a localization software to accurately navigate the AIBO
Curvature-Based Environment Description for Robot Navigation Using Laser Range Sensors
This work proposes a new feature detection and description approach for mobile robot navigation using 2D laser range sensors. The whole process consists of two main modules: a sensor data segmentation module and a feature detection and characterization module. The segmentation module is divided in two consecutive stages: First, the segmentation stage divides the laser scan into clusters of consecutive range readings using a distance-based criterion. Then, the second stage estimates the curvature function associated to each cluster and uses it to split it into a set of straight-line and curve segments. The curvature is calculated using a triangle-area representation where, contrary to previous approaches, the triangle side lengths at each range reading are adapted to the local variations of the laser scan, removing noise without missing relevant points. This representation remains unchanged in translation or rotation, and it is also robust against noise. Thus, it is able to provide the same segmentation results although the scene will be perceived from different viewpoints. Therefore, segmentation results are used to characterize the environment using line and curve segments, real and virtual corners and edges. Real scan data collected from different environments by using different platforms are used in the experiments in order to evaluate the proposed environment description algorithm
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