共查询到20条相似文献,搜索用时 609 毫秒
1.
An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular
map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed
by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using
a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful
topological information in real-time and is robust to environment change, because this map is extracted from a local grid
map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative
reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps,
a global topological map should be constructed by merging local topological maps according to nodes with high position probability.
For real and complex environments, experiments showed that the proposed map building method based on the thinning process
can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally
constructed. 相似文献
2.
This paper presents a method of topological localization with kidnap recovery capability in a home environment using only low-cost sonar sensors. The proposed method considers both pose tracking and relocation problems. The pose tracking is achieved by calculating node probability using grid-map matching and relative motion model. The relocation method detects the kidnap automatically and recovers it using multiple hypothesis tracking. After kidnap recovery, it also provides a criterion for selecting a reasonable hypothesis for returning to the pose tracking stage autonomously. Experimental results in a real home environment verify that the proposed localization method provides a reliable and convergent node probability when the robot is kidnapped. 相似文献
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Li Maohai Wang Han Sun Lining Cai Zesu 《Engineering Applications of Artificial Intelligence》2013,26(8):1942-1952
Robust topological navigation strategy for omnidirectional mobile robot using an omnidirectional camera is described. The navigation system is composed of on-line and off-line stages. During the off-line learning stage, the robot performs paths based on motion model about omnidirectional motion structure and records a set of ordered key images from omnidirectional camera. From this sequence a topological map is built based on the probabilistic technique and the loop closure detection algorithm, which can deal with the perceptual aliasing problem in mapping process. Each topological node provides a set of omnidirectional images characterized by geometrical affine and scale invariant keypoints combined with GPU implementation. Given a topological node as a target, the robot navigation mission is a concatenation of topological node subsets. In the on-line navigation stage, the robot hierarchical localizes itself to the most likely node through the robust probability distribution global localization algorithm, and estimates the relative robot pose in topological node with an effective solution to the classical five-point relative pose estimation algorithm. Then the robot is controlled by a vision based control law adapted to omnidirectional cameras to follow the visual path. Experiment results carried out with a real robot in an indoor environment show the performance of the proposed method. 相似文献
5.
《Advanced Robotics》2013,27(1-2):185-201
This paper presents a method for building a probability grid map for autonomous mobile robots with ultrasonic sensors using a data association filter (DAF). The method is based on evaluating the possibility that the acquired sonar data are all reflected by the same object. The DAF is able to associate data points with each other. Data affected by specular reflection are not likely to be associated with the same object, so they are excluded from the data cluster by the DAF, thereby improving the reliability of the data used for the probability grid map. Since the corrupted data are not used to update the probability map, it is possible to build a good quality grid map even in a specular environment. The DAF was applied to the Bayesian and the Orientation probability models, which are typical models used to build grid maps, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a real-world environment. 相似文献
6.
《Robotics and Autonomous Systems》2007,55(2):162-175
In this paper, a modified method for occupancy grid map building by a moving mobile robot and a scanning ultrasonic range-finder is proposed. The map building process consists of two phases: (1) gleaning of information from environment, and (2) sonar data processing. For sonar data processing the proposed modified method combines: (1) statistical approach for probability sonar model building; and (2) application of fuzzy logic theory for sonar data fusion. It is experimentally shown that, in some applications, the proposed modified method has advantages over other well-known methods. 相似文献
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This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments. 相似文献
8.
This paper describes a method for absolute localization and environment recognition for an autonomous, sonar-equipped robot. The addition of an auto-associative memory to previously developed non-neural map making software results in a system that is capable of recognizing its environment and its position within the environment using remembered features and room geometry. In the prior system the robot used sonar to construct a metric map of an environment, but the map information had to be reconstructed each time the robot returned to an environment. We evaluated the system with a task that requires memory of the position of a goal that is not directly detectable by sonar. 相似文献
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In behavior‐based robots, planning is necessary to elaborate abstract plans that resolve complex navigational tasks. Usually maps of the environment are used to plan the robot motion and to resolve the navigational tasks. Two types of maps have been mainly used: metric and topological maps. Both types present advantages and weakness so that several integration approaches have been proposed in literature. However, in many approaches the integration is conducted to build a global representation model, and the planning and navigational techniques have not been fitted to profit from both kinds of information. We propose the integration of topological and metric models into a hybrid deliberative‐reactive architecture through a path planning algorithm based on A* and a hierarchical map with two levels of abstraction. The hierarchical map contains the required information to take advantage of both kinds of modeling. On one hand, the topological model is based on a fuzzy perceptual model that allows the robot to classify the environment in distinguished places, and on the other hand, the metric map is built using regions of possibility with the shape of fuzzy segments, which are used later to build fuzzy grid‐based maps. The approach allows the robot to decide on the use of the most appropriate model to navigate the world depending on minimum‐cost and safety criteria. Experiments in simulation and in a real office‐like environment are shown for validating the proposed approach integrated into the navigational architecture. © 2002 Wiley Periodicals, Inc. 相似文献
11.
《Advanced Robotics》2013,27(1-2):179-206
The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm. 相似文献
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In this paper, a new network is proposed for automated recognition and classification of the environment information into regions, or nodes. Information is utilized in learning the topological map of an environment. The architecture is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) that comprises of two layers, input and memory. The input layer is formed using the Multiple Bayesian Adaptive Resonance Theory, which collects sensory data and incrementally clusters the obtained information into a set of nodes. In the memory layer, the clustered information is used as a topological map, where nodes are connected with edges. Nodes in the topological map represent regions of the environment and stores the robot location, while edges connect nodes and stores the robot orientation or direction. The proposed method, a Multi-channel Bayesian Adaptive Resonance Associative Memory (MBARAM) is validated using a number of benchmark datasets. Experimental results indicate that MBARAM is capable of generating topological map online and the map can be used for localization. 相似文献
13.
《Advanced Robotics》2013,27(6-7):941-962
In this paper we present an algorithm for the application of simultaneous localization and mapping in complex environments. Instead of building a grid map or building a feature map with a small number of the obstacles' geometric parameters, the proposed algorithm builds a sampled environment map (SEM) to represent a complex environment with a set of environment samples. To overcome the lack of one-toone correspondence between environment samples and raw observations, the signed orthogonal distance function is proposed to be used as the observation model. A method considering geometric constraints is presented to remove redundant environment samples from the SEM. We also present a method to improve the SEM's topological consistency by using corner constraints. The proposed algorithm has been verified in a simulation and an indoor experiment. The results show that the algorithm can localize the robot and build a complex map effectively. 相似文献
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《Advanced Robotics》2013,27(2-3):339-359
A grid map can be efficiently used in navigation, but this type of map requires a large amount of memory in proportion to the size of the environment. As an alternative, a topological map can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is built based on the local grid map by using a thinning algorithm. This new approach can easily extract the topological information in real-time and be robustly applicable to the real environment, and this map can be autonomously built by exploration. The position possibility is defined to evaluate the quantitative reliability of the topological map and then a new exploration scheme based on the position possibility is proposed. From the position possibility information, the robot can determine whether or not it needs to visit a specific end node, which node will be the next target and how much of the environment has yet been explored. Various experiments showed that the proposed map-building and exploration methods can accurately build a local topological map in real-time and can guide a robot safely even in a dynamic environment. 相似文献
16.
Joong-Tae Park Jae-Bok Song Se-Jin Lee Munsang Kim 《Journal of Intelligent and Robotic Systems》2011,63(3-4):465-480
This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments. 相似文献
17.
Appearance-based mapping and localization for mobile robots using a feature stability histogram 总被引:1,自引:0,他引:1
The strength of appearance-based mapping models for mobile robots lies in their ability to represent the environment through high-level image features and to provide human-readable information. However, developing a mapping and a localization method using these kinds of models is very challenging, especially if robots must deal with long-term mapping, localization, navigation, occlusions, and dynamic environments. In other words, the mobile robot has to deal with environmental appearance change, which modifies its representation of the environment. This paper proposes an indoor appearance-based mapping and a localization method for mobile robots based on the human memory model, which was used to build a Feature Stability Histogram (FSH) at each node in the robot topological map. This FSH registers local feature stability over time through a voting scheme, and the most stable features were considered for mapping, for Bayesian localization and for incrementally updating the current appearance reference view in the topological map. The experimental results are presented using an omnidirectional images dataset acquired over the long-term and considering: illumination changes (time of day, different seasons), occlusions, random removal of features, and perceptual aliasing. The results include a comparison with the approach proposed by Dayoub and Duckett (2008) [19] and the popular Bag-of-Words (Bazeille and Filliat, 2010) [35] approach. The obtained results confirm the viability of our method and indicate that it can adapt the internal map representation over time to localize the robot both globally and locally. 相似文献
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This paper presents a novel approach to the vision based grid map building and localization problem that works in a complex
indoor environment with a single forward viewing camera. Most existing visual SLAM has been limited to the feature-based method
and only a few researchers have proposed visual SLAM methods for building a grid map using a stereo vision system which has
not been popular in practical application. In this paper, we estimate the planar depth by applying a simple visual sonar ranging
technique to the single camera image and then associating sequential scans through our own pseudo dense adaptive scan matching
algorithm reducing the processing time compared to the standard point-to-point correspondence based algorithm and finally
produce a grid map. To this end, we construct a Pseudo Dense Scan (PDS) which is an odometry based temporal accumulation of
the visual sonar readings emulating omni-directional sensing in order to overcome the sparseness of the visual sonar. Moreover,
in order to obtain a much more refined map, we further correct the slight trajectory error incurred in the PDS construction
step using Sequential Quadratic Programming (SQP) which is a well-known optimization scheme. Experimental results show that
our method can obtain an accurate grid map using a single camera without the need for a high price range sensors or stereo
camera.
相似文献
Se-Young OhEmail: |