Efficient, iterative, sensor based 3-D map building using rating functions in configuration space

Kruse, E.; Gutsche, Ralf; Wahl, Friedrich M.

doi:10.1109/robot.1996.506850

Cited by 45 publications

(32 citation statements)

References 11 publications

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“…Much work concerning three-dimensional environment models has already been done, but it mostly focuses on wheel-driven vehicles (e.g. Stuck et al 1994;Kruse et al 1996;Murphy et al 2002). All of these use some sort of occupancy grid (Elfes 1989) for representation, which proved to be very effective, but none of these approaches considers the specific needs of a walking robot.…”

Section: Perceptionmentioning

confidence: 99%

Biologically inspired walking machines: design, control and perception

Dillmann

Albiez

Gaßmann

et al. 2006

Phil. Trans. R. Soc. A.

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This article presents a set of methods used to support the design and control of biologically inspired walking machines. Starting with a description of the general system design idea, an example for the design of the mechanical construction, a computer supported design procedure for the control architecture and the description of a threedimensional world model to be used as knowledge base is given. The focus of this paper is on the engineering and integration process and the interrelation between the different phases of the design process.

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Section: Perceptionmentioning

confidence: 99%

Biologically inspired walking machines: design, control and perception

Dillmann

Albiez

Gaßmann

et al. 2006

Phil. Trans. R. Soc. A.

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“…In SLAM, a robot localizes itself as it maps the environment. Researchers have addressed this problem for well-structured (indoor) environments and have obtained important results (Anousaki et al 1999, Castellanos et al 1998, Kruse et al 1996, Leonard et al 1991, Thrun et al 2000, Tomatis et al 2001). These algorithms have been implemented for several different sensing methods, such as stereo camera vision systems (Castellanos et al 1998, Se et al 2002, laser range sensors (Tomatis et al 2001), and ultrasonic sensors (Anousaki et al 1999, Leonard et al 1991.…”

Section: Introductionmentioning

confidence: 99%

A new technique in mobile robot simultaneous localization and mapping

Sujan

Meggiolaro

Belo

2006

Sba Controle & Automação

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In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite. KEYWORDS:Service robots, visual mapping, selflocalization, information theory, Mellin transform. RESUMOUsualmente não é possível fornecer a priori a robôs móveis autônomos um mapa detalhado de seu ambiente de trabalho. Nestes casos, o robô precisa criar um modelo geométrico preciso movendo-se pelo ambiente e utilizando seus sensores. Neste trabalho, um algoritmo iterativo é proposto para planejar a estratégia de exploração de robôs móveis autôno-mos, permitindo-os construir de forma eficiente um modelo do ambiente em forma de grafo sem a necessidade de sensores de alto custo. Neste algoritmo, o conteúdo de informação presente em sub-regiões de uma imagem panorâmica 2-D do ambiente é determinada a partir da posição atual do robô usando uma única câmera fixada em sua estrutura. Usando uma métrica baseada na teoria da informação de Shannon, o algoritmo determina, a partir da imagem 2-D, localizações potenciais para novos nós do grafo, a partir dos quais serão tomadas novas imagens panorâmicas para prosseguir com a exploração. Uma transformada de Mellin é usada para guiar o robô de volta a um nó previamente explorado. Este processo continua até que todo o ambiente tenha sido explorado em um nível de detalhes pré-especificado. A eficácia do algoritmo é verificada experimentalmente através da exploração de um ambiente interno por um agente robótico móvel dispondo apenas de um conjunto limitado de sensores. PALAVRAS-CHAVE:Robôs móveis, mapeamento visual, auto-localização, teoria da informação, transformada de Mellin.

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“…Wahl [58] describes a more useful function for active sensor deployment. A "planning-sensing-updating" cycle is described, which based on the current, incomplete model and motion constraints, computes the next-best view such that an objective function in configuration space is maximized.…”

mentioning

confidence: 99%

Extending the Path-Planning Horizon

Nabbe¹,

Hebert

2007

The International Journal of Robotics Research

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SINCE typical mobile robotic vehicles have mobility sensors (such as LADAR or stereo) that can only acquire data up to a few tens of meters, a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and has no choice than to plan inefficient paths that trace obstacle boundaries. To alleviate this problem, We present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a "what-if" analysis of hypothetical future configurations of the environment. Candidate vantage positions are evaluated based on their ability of observing anticipated obstacles. These vantage positions identified by this forward-simulation framework are used by the planner as intermediate waypoints.The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results also show that opportunistically significant reduction in path length can be achieved by using this framework. The reconstructed epipolar geometry from the very wide baseline100 A.9The ratio of the height over the width of the window on the left is 1.5456 and the window on the right 1.8356, which gives us an indication of the reconstruction quality. This effect is due to the fact that the planner is limited by the maximum range of the mobility sensors. Since typical mobility sensors, such as Laser Radar (LADAR) or passive stereo vision, will only acquire data up to a few tens of meters, the planner has no knowledge about what to encounter beyond the sensed perimeter. As a result, the planner is unable to anticipate obstacles sufficiently early and has no choice but to plan paths close to obstacle boundaries.For autonomous navigation over long distances, this issue degrades the performance of the system by greatly increasing the length of the path traveled by the vehicle. Consequently, the power consumed is increased and, more importantly, CHAPTER 1. INTRODUCTION FIGURE 1.1. Typical example of poor performance due to lack of sensor planning and mid-range sensing (Left: Overhead view of terrain; Right: Executed path with detected obstacles shown as shaded regions). The path from S1 to G1 intersects a large hill which is discovered only when the vehicle enters a large cul-de-sac, causing the executed path to be substantially more expensive than the path that would have been followed, had the obstruction been discovered earlier. (From [114]) the risk of exposure to threats is also increased. In addition, the relative short range of the mobility sensors forces the vehicle to drive closer to terrain obstructions than is safe or necessary.One solution is to use sensors with longer range to acquire information about the terrain further ahead. However, to use these sensors one needs to take into account certain constraints about the sensing geom...

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Efficient, iterative, sensor based 3-D map building using rating functions in configuration space

Cited by 45 publications

References 11 publications

Biologically inspired walking machines: design, control and perception

Biologically inspired walking machines: design, control and perception

A new technique in mobile robot simultaneous localization and mapping

Extending the Path-Planning Horizon

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