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

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.

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 particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments.

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 a data-driven approach to tackle the Visual-SLAM problem. This review summarises recent advancements in the Visual-SLAM domain using various learning-based methods. We begin by providing a concise overview of the geometric model-based approaches, followed by technical reviews on the current paradigms in SLAM. Then, we present the various learning-based approaches to collecting sensory inputs from mobile robots and performing scene understanding. The current paradigms in deep-learning-based semantic understanding are discussed and placed under the context of Visual-SLAM. Finally, we discuss challenges and further opportunities in the direction of learning-based approaches in Visual-SLAM.

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 introduced and advanced a novel concept of an underwater robotic system composed of a number of mobile robot modules (nodes), carrying complementary sensors for perception of the environment. Instead of being physically coupled, the modules are connected via communication links that allow a flow of essential information among them. Without rigid links, the so-called MORPH Supra-Vehicle can reconfigure itself and adapt according to the environment and mission goals, responding, for example, to the shape of the terrain, including vertical walls. The flexibility allows for more optimal positioning of each sensor, increased number of simultaneous viewpoints, and generally high-resolution data collection.MORPH is aimed at providing a proof-of-concept demonstration of such capabilities, an effort that includes technological developments in many of the subfields of underwater technology. The main results are summarized and presented in this paper.<def-list>Abbreviation List<def-item><term>AUV</term><def>autonomous underwater vehicles</def></def-item><def-item><term>CV</term><def>camera vehicle</def></def-item><def-item><term>CWC</term><def>cold water corals</def></def-item><def-item><term>GCV</term><def>global navigation and communications vehicle</def></def-item><def-item><term>ICP</term><def>iterative closest point method</def></def-item><def-item><term>LSV</term><def>local sonar vehicle</def></def-item><def-item><term>MBES</term><def>multibeam echosounder</def></def-item><def-item><term>MCL</term><def>mission control language</def></def-item><def-item><term>PF</term><def>path following</def> </def-item><def-item><term>PI</term><def>principal investigator</def></def-item><def-item><term>ROF</term><def> range-only formation</def></def-item><def-item><term>ROS</term><def>Robot Operation System</def></def-item><def-item><term>SSV</term><def>surface support vessel</def></def-item> <def-item> <term>TDMA</term> <def> time division multiple access </def> </def-item><def-item><term>USBL</term><def>ultra-short baseline (navigation)</def></def-item> <def-item> <term>UUV</term> <def> unmanned underwater vehicle </def> </def-item><def-item><term>VCS</term><def>version control system</def></def-item></def-list>

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. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n�) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n�) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

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. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n3) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n2) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

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, low-cost sensor is a digital camera, which captures images that can be used to recognise where the robot is, and to incrementally position the robot as it moves. SLAM using a single camera is challenging however, and many contemporary schemes suffer complete failure in dynamic or featureless environments, or during erratic camera motion. An additional problem, known as scale drift, is that cameras do not directly measure the scale of the environment, and errors in relative scale accumulate over time, introducing errors into the robot’s speed and position estimates. Key to a successful visual SLAM system is the ability to continue operation despite these difficulties, and to recover from positioning failure when it occurs. This thesis describes the development of such a scheme, which is known as BoWSLAM. BoWSLAM enables a robot to reliably navigate and map previously unknown environments, in real-time, using only a single camera. In order to position a camera in visually challenging environments, BoWSLAM combines contemporary visual SLAM techniques with four new components. Firstly, a new Bag-of-Words (BoW) scheme is developed, which allows a robot to recognise places it has visited previously, without any prior knowledge of its environment. This BoW scheme is also used to select the best set of frames to reconstruct positions from, and to find efficient wide-baseline correspondences between many pairs of frames. Secondly, BaySAC, a new outlier- robust relative pose estimation scheme based on the popular RANSAC framework, is developed. BaySAC allows the efficient computation of multiple position hypotheses for each frame. Thirdly, a graph-based representation of these position hypotheses is proposed, which enables the selection of only reliable position estimates in the presence of gross outliers. Fourthly, as the robot explores, objects in the world are recognised and measured. These measurements enable scale drift to be corrected. BoWSLAM is demonstrated mapping a 25 minute 2.5km trajectory through a challenging and dynamic outdoor environment in real-time, and without any other sensor input; considerably further than previous single camera SLAM schemes.

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 localisation for mobile robot navigation. Topological maps are concise and graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well-suited for basic robot navigation applications, and also provide a representational basis for the procedural and semantic information needed for higher-level robotic tasks. In order to make vision-based topological navigation suitable for inexpensive mobile robots for the mass market we propose to characterise key places of the environment based on their visual appearance through colour histograms. The approach for representing places using visual appearance is based on the fact that colour histograms change slowly as the field of vision sweeps the scene when a robot moves through an environment. Hence, a place represents a region of the environment rather than a single position. We demonstrate in experiments using an indoor data set, that a topological map in which places are characterised using visual appearance augmented with metric clues provides sufficient information to perform continuous metric localisation which is robust to the kidnapped robot problem. Many topological mapping methods build a topological map by clustering visual observations to places. However, due to perceptual aliasing observations from different places may be mapped to the same place representative in the topological map. A main contribution of this thesis is a novel approach for dealing with the perceptual aliasing problem in topological mapping. We propose to incorporate neighbourhood relations for disambiguating places which otherwise are indistinguishable. We present a constraint based stochastic local search method which integrates the approach for place disambiguation in order to induce a topological map. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that a small map is found quickly. Moreover, the method of using neighbourhood information for place disambiguation is integrated into a framework for topological off-line simultaneous localisation and mapping which does not require an initial categorisation of visual observations. Experiments on an indoor data set demonstrate the suitability of our method to reliably localise the robot while building a topological map.

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 rehearsed, they become part of the LTM (i.e. they become more stable). However, this work proposes a different memory model, allowing to any input be part of the STM or LTM considering the input strength. The most stable features are only used for SLAM. This innovative feature management approach is able to cope with changing environments, and long-term SLAM.
Este trabajo propone un método de SLAM basado en apariencia cuya principal contribución es el Histograma de Estabilidad de Características (FSH). El FSH es construido por votación, si una característica es re-observada, ésta será promovida; de lo contrario su valor FSH progresivamente es reducido. El FSH es basado en el modelo de memoria humana para ocuparse de ambientes cambiantes y SLAM a largo término. Este modelo introduce conceptos como memoria a corto plazo (STM) y largo plazo (LTM), las cuales retienen información por cortos y largos periodos de tiempo. Si una entrada a la STM es reforzada, ésta hará parte de la LTM (i.e. es más estable). Sin embargo, este trabajo propone un modelo de memoria diferente, permitiendo a cualquier entrada ser parte de la STM o LTM considerando su intensidad. Las características más estables son solamente usadas en SLAM. Esta innovadora estrategia de manejo de características es capaz de hacer frente a ambientes cambiantes y SLAM de largo término.