Hybrid simultaneous localization and map building: a natural integration of topological and metric

Tomatis, N.; Nourbakhsh, Illah; Siegwart, Roland

doi:10.1016/s0921-8890(03)00006-x

Cited by 141 publications

(88 citation statements)

References 16 publications

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“…Tomatis et al [16], Kouzoubov and D. Austin [10], Bosse et al [6], and Blanco et al [5] used hybrid approaches to connect local metric submaps using high level topological maps. In all cases, the fusion aims at segmenting metric maps represented by topological nodes in order to organize and identify submap relations and loop closures.…”

Section: Related Workmentioning

confidence: 99%

Real-time topometric localization

Badino

Huber

Kanade

2012

2012 IEEE International Conference on Robotics and Automation

158

111

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Abstract-Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a realtime method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating the route using a GPS-equipped vehicle and building a compact database of simple visual and 3D features. We then localize using a Bayesian filter to match sequences of visual or range measurements to the database. The algorithm is reliable across wide environmental changes, including lighting differences, seasonal variations, and occlusions, achieving an average localization accuracy of 1 m over an 8 km route. The method converges correctly even with wrong initial position estimates solving the kidnapped robot problem.

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Section: Related Workmentioning

confidence: 99%

Real-time topometric localization

Badino

Huber

Kanade

2012

2012 IEEE International Conference on Robotics and Automation

158

111

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“…In (Simhon & Dudek, 1998) the environment is represented by a hybrid topological-metric map composed by a set of local metric maps called islands of reliability. (Tomatis et al, 2003) describes the environment using a global topological map that associates places which are metrically represented by infinite lines belonging to the same places. (Van Zwynsvoorde et al, 2000) constructs a topological representation as a route graph using Voronoï diagrams.…”

Section: Environment Modeling Using Visionmentioning

confidence: 99%

Attentional Behaviors for Environment Modeling by a Mobile Robot

Bachiller¹,

Bustos²,

Manso³

2011

Advances in Stereo Vision

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Building robots capable of interacting in an effective and autonomous way with their environments requires to provide them with the ability to model the world. That is to say, the robot must interpret the environment not as a set of points, but as an organization of more complex structures with human-like meaning. Among the variety of sensory inputs that could be used to equip a robot, vision is one of the most informative ones. Through vision, the robot can analyze the appearance of objects. The use of stereo vision also gives the possibility to extract spatial information of the environment, allowing to determine the structure of the different elements composing it. However, vision suffers from some limitations when it is considered in isolation. On one hand, cameras have a limited field of view that can only be compensated through camera movements. On the other hand, the world is formed by non-convex structures that can only be interpreted by actively exploring the environment. Hence, the robot must move its head and body to give meaning to perceived elements composing its environment. The combination of stereo vision and active exploration provides a means to model the world. While the robot explores the environment perceived regions can be clustered, forming more complex structures like walls and objects on the floor. Nevertheless, even in simple scenarios with few rooms and obstacles, the robot must be endowed with different abilities to successfully solve the task. For instance, during exploration, the robot must be able to decide where to look at while selecting where to go, avoiding obstacles and detecting what is that it is looking at. From the point of view of perception, there are different visual behaviors that take part in this process, such as those related to look towards what the robot can recognize and model, or those dedicated to maintain itself within safety limits. From the action perspective, the robot has to move in different ways depending on internal states (i.e. the status of the modeling process) and external situations (i.e. obstacles in the way to a target position). Both perception and action should influence each other in such a way that deciding where to look at depends on what the robot is doing, but also in a way that what is being perceived determines what the robot can or can not do. Our solution to all these questions relies heavily on visual attention. Specifically, the foundation of our proposal is that attention can organize the perceptual and action processes by acting as an intermediary between both of them. The attentional connection allows, on one hand, to drive the perceptual process according to the behavioral requirements and, on the other hand, to modulate actions on the basis of the perceptual results of the attentional control. Thus, attention solves the where to look problem and, additionally, attention prevents

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“…Detection of doors is of great importance for various navigation and manipulation tasks. The doors are often places which separate different locations, can be used as landmarks for navigation and/or relative positioning or waypoints to guide exploration and SLAM strategies [1]. They also need to be recognized for door opening and navigation to neighbouring rooms [2,3].…”

Section: Introductionmentioning

confidence: 99%

Visual door detection integrating appearance and shape cues

Murillo

Košecká

Guerrero

et al. 2008

Robotics and Autonomous Systems

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Important component of human-robot interaction is the capability to associate semantic concepts to encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Different from the previous approaches the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about structure of indoors environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environments, showing a good performance providing the door extent in images is sufficiently large and well supported by low level feature measurements.

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Hybrid simultaneous localization and map building: a natural integration of topological and metric

Cited by 141 publications

References 16 publications

Real-time topometric localization

Real-time topometric localization

Attentional Behaviors for Environment Modeling by a Mobile Robot

Visual door detection integrating appearance and shape cues

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