Relevant bibliographies by topics / Mobile Robot;Simultaneous Localisation and Mapping;AUV

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  1. Journal articles
  2. Dissertations / Theses
  3. Books

Academic literature on the topic 'Mobile Robot;Simultaneous Localisation and Mapping;AUV'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Mobile Robot;Simultaneous Localisation and Mapping;AUV"

1

Panzieri, Stefano, Federica Pascucci, and Roberto Setola. "Simultaneous localisation and mapping of a mobile robot via interlaced extended Kalman filter." International Journal of Modelling, Identification and Control 4, no. 1 (2008): 68. http://dx.doi.org/10.1504/ijmic.2008.021001.

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2

Adamowicz, Mateusz, Leszek Ambroziak, and Mirosław Kondratiuk. "Efficient Non-Odometry Method for Environment Mapping and Localisation of Mobile Robots." Acta Mechanica et Automatica 15, no. 1 (2021): 24–29. http://dx.doi.org/10.2478/ama-2021-0004.

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Abstract The paper presents the simple algorithm of simultaneous localisation and mapping (SLAM) without odometry information. The proposed algorithm is based only on scanning laser range finder. The theoretical foundations of the proposed method are presented. The most important element of the work is the experimental research. The research underlying the paper encompasses several tests, which were carried out to build the environment map to be navigated by the mobile robot in conjunction with the trajectory planning algorithm and obstacle avoidance.
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3

Lei, Xu, Bin Feng, Guiping Wang, Weiyu Liu, and Yalin Yang. "A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot." Electronics 9, no. 4 (2020): 695. http://dx.doi.org/10.3390/electronics9040695.

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The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of
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4

Lai, Tin. "A Review on Visual-SLAM: Advancements from Geometric Modelling to Learning-Based Semantic Scene Understanding Using Multi-Modal Sensor Fusion." Sensors 22, no. 19 (2022): 7265. http://dx.doi.org/10.3390/s22197265.

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Simultaneous Localisation and Mapping (SLAM) is one of the fundamental problems in autonomous mobile robots where a robot needs to reconstruct a previously unseen environment while simultaneously localising itself with respect to the map. In particular, Visual-SLAM uses various sensors from the mobile robot for collecting and sensing a representation of the map. Traditionally, geometric model-based techniques were used to tackle the SLAM problem, which tends to be error-prone under challenging environments. Recent advancements in computer vision, such as deep learning techniques, have provided
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5

Kalwa, Joerg, Daniel Tietjen, Marina Carreiro-Silva, et al. "The European Project MORPH: Distributed UUV Systems for Multimodal, 3D Underwater Surveys." Marine Technology Society Journal 50, no. 4 (2016): 26–41. http://dx.doi.org/10.4031/mtsj.50.4.10.

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AbstractThe MORPH project (FP 7, 2012‐2016) is aimed at developing efficient methods and tools to map the underwater environment in situations that are not easily addressed by current technology. Namely, the missions that are of interest are those that involve underwater surveying and marine habitat mapping of rugged terrain and structures with full 3D complexity, including vertical cliffs. Potential applications include the study of cold water coral reef communities, ecosystems from underwater canyons, pipeline and harbor monitoring, or the inspection of wind turbine foundations. The project
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Dissertations / Theses on the topic "Mobile Robot;Simultaneous Localisation and Mapping;AUV"

1

Williams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." University of Sydney. Aerospace, Mechanical and Mechatronic Engineering, 2002. http://hdl.handle.net/2123/809.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. Fr
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2

Williams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." Thesis, The University of Sydney, 2001. http://hdl.handle.net/2123/809.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. Fr
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3

Botterill, Tom. "Visual navigation for mobile robots using the Bag-of-Words algorithm." Thesis, University of Canterbury. Computer Science and Software Engineering, 2011. http://hdl.handle.net/10092/5511.

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Robust long-term positioning for autonomous mobile robots is essential for many applications. In many environments this task is challenging, as errors accumulate in the robot’s position estimate over time. The robot must also build a map so that these errors can be corrected when mapped regions are re-visited; this is known as Simultaneous Localisation and Mapping, or SLAM. Successful SLAM schemes have been demonstrated which accurately map tracks of tens of kilometres, however these schemes rely on expensive sensors such as laser scanners and inertial measurement units. A more attractive, l
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4

Werner, Felix. "Vision-based topological mapping and localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/31815/1/Felix_Werner_Thesis.pdf.

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Competent navigation in an environment is a major requirement for an autonomous mobile robot to accomplish its mission. Nowadays, many successful systems for navigating a mobile robot use an internal map which represents the environment in a detailed geometric manner. However, building, maintaining and using such environment maps for navigation is difficult because of perceptual aliasing and measurement noise. Moreover, geometric maps require the processing of huge amounts of data which is computationally expensive. This thesis addresses the problem of vision-based topological mapping and loca
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5

Bacca, Cortés Eval Bladimir. "Appearance-based mapping and localization using feature stability histograms for mobile robot navigation." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/83589.

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This work proposes an appearance-based SLAM method whose main contribution is the Feature Stability Histogram (FSH). The FSH is built using a voting schema, if the feature is re-observed, it will be promoted; otherwise it progressively decreases its corresponding FSH value. The FSH is based on the human memory model to deal with changing environments and long-term SLAM. This model introduces concepts of Short-Term memory (STM), which retains information long enough to use it, and Long-Term memory (LTM), which retains information for longer periods of time. If the entries in the STM are rehears
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Books on the topic "Mobile Robot;Simultaneous Localisation and Mapping;AUV"

1

Milford, Michael John. Robot navigation from nature: Simultaneous localisation, mapping, and path planning based on hippocampal models. Springer, 2008.

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2

Adaptive Sampling With Mobile Wsn Simultaneous Robot Localisation And Mapping Of Paramagnetic Spatiotemporal Fields. Institution of Engineering & Technology (IET), 2011.

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3

Lewis, Frank L., Koushil Sreenath, Muhammad F. Mysorewala, and Dan O. Popa. Adaptive Sampling with Mobile WSN: Simultaneous Robot Localisation and Mapping of Paramagnetic Spatio-Temporal Fields. Institution of Engineering & Technology, 2011.

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4

Milford, Michael John. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer, 2010.

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5

Milford, Michael John. Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models. Springer London, Limited, 2007.

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