Localization in a wide range of industrial environments using relative 3D ceiling features

Lecking, Daniel; Wulf, Oliver; Wagner, Bernd

doi:10.1109/etfa.2008.4638415

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Although an accurate 3D model of the environment in not required, an accurate and consistent prior is always desired when the localization is integrated with other navigation functions. Similarly in [181,182], a 3D point cloud of the environment is obtained by servoing a 2D LIDAR, and extracted 2D features are used to perform localization. This method has been shown to work well in an indoor environment with well structured ceiling features.…”

Section: Localizationmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Localizationmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Tae‐Bum et al () proposed an MCL with topological information to reduce the number of random samples used for the initial phase. In (Wulf, Lecking, and Wagner () and Lecking, Wulf, and Wagner (), the MCL system uses ceiling features measured with a three‐dimensional (3D) laser scanner to overcome the sensor occlusion problem. Fox (), Kwok, Fox, and Meila (), and Heinemann, Haase, and Zell () proposed MCL alternatives that can dynamically adjust the size of the samples when they are used to track the robot pose (but they are not fit for solving the global localization problem).…”

Section: State Of the Artmentioning

confidence: 99%

Observation‐driven Bayesian Filtering for Global Location Estimation in the Field Area

Hirose

2013

Journal of Field Robotics

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Global localization has long been considered one of the most important but also one of the most challenging localization problems for mobile robots. Current studies of global localization in the literature are mainly based on the Bayesian filtering technique, which can provide an elegant statistical framework for uncertainty management and multisensory fusion. However, the majority of implementations of Bayesian filters for global localization obey the same update rules in such a location‐driven sense that they guess the robot location first and then adjust the guess by incorporating the current observation data. This leads to some problematic consequences in that the system suffers from great computational load in a large application area and it cannot recover from localization failure. To overcome the above limitations, this paper deviates from the conventional update rules of Bayes filters and proposes a new approach: the observation‐driven Bayes filters (OD‐BFs). As the name implies, OD‐BFs estimate the robot state just according to the most recent observations and then adjust the estimate by incorporating the dead‐reckoning information. We further implement an observation‐driven Bayes filter to globally estimate the robot pose in the field area. This global localization system features an effective pose estimation framework that can operate with a large amount of point data in a coarse‐to‐fine manner. Sufficient experiments were carried out to determine both the advantages and the disadvantages of our OD‐BF localization approaches compared with previous ones.

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“…Real world objects, such as obstacles (including moving obstacles) or desired arrival points are processed in this way. After recognition of the obstacles, and automatic localization (C. Eberst and Christensen, 2000), (D. Lecking and Wagner, 2008), (S. Kolski and Siegwart, 2006), the smart controller evaluates several short-term strategies and sends the best one to the robots real-time controller. It receives the advices as commands and has two alternatives: ignore or take them according to robots current priorities.…”

Section: Rta Controllermentioning

confidence: 99%

Performance Evaluation for Autonomous Mobile Robots

Trejo¹,

Biedma²,

Luise

et al. 2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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The aim of this paper is to implement metrics and to define indicators to provide a unified criteria for evaluation method of the performance of autonomous mobile robots using different control algorithms. There is a first description of the mobile robot problem and the importance of a standardized process for evaluation of robot performances. There is a comparison between a simple navigation controller and an intelligent prototype based on consciousness. The architecture main features are also outlined. Test cases with and without conscious controller show that the latter performs an optimized source-to-target path.

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Localization in a wide range of industrial environments using relative 3D ceiling features

Cited by 8 publications

References 7 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Observation‐driven Bayesian Filtering for Global Location Estimation in the Field Area

Performance Evaluation for Autonomous Mobile Robots

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