Image‐based path planning for outdoor mobile robots

Huang, Wesley; Ollis, Mark; Happold, Michael; Stancil, Brian

doi:10.1002/rob.20272

Cited by 11 publications

(6 citation statements)

References 23 publications

(24 reference statements)

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“…Another approach to the path following problem, that also uses a RGB-D monocular camera, is presented by Huang et al [9]. They combine the range-limited depth information with (long-range) RGB imagery to create cost maps which are subsequently used to plan the robot path.…”

Section: Related Workmentioning

confidence: 99%

Visual road following using intrinsic images

Krajník

Blazicek

Santos

2015

2015 European Conference on Mobile Robots (ECMR)

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Abstract-We present a real-time visual-based road following method for mobile robots in outdoor environments. The approach combines an image processing method, that allows to retrieve illumination invariant images, with an efficient path following algorithm. The method allows a mobile robot to autonomously navigate along pathways of different types in adverse lighting conditions using monocular vision.To validate the proposed method, we have evaluated its ability to correctly determine boundaries of pathways in a challenging outdoor dataset. Moreover, the method's performance was tested on a mobile robotic platform that autonomously navigated long paths in urban parks. The experiments demonstrated that the mobile robot was able to identify outdoor pathways of different types and navigate through them despite the presence of shadows that significantly influenced the paths' appearance.

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Section: Related Workmentioning

confidence: 99%

Visual road following using intrinsic images

Krajník

Blazicek

Santos

2015

2015 European Conference on Mobile Robots (ECMR)

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“…Given the irregularities of outdoor locations, terrain classification is an important task in order for the robot to avoid moving away from the path [189]. In [190], the authors present an image-based path planning method for outdoor terrain environments. Moving objects (e.g., animals, pedestrians To cope with all these different challenges (and also to find standard vision-based solutions), general feature-based approaches are commonly used for SLAM, as in [191] and [71], and also for navigation applications [112].…”

Section: Outdoor Locationsmentioning

confidence: 99%

“…(d) Lastly, self-calibration techniques do not use any calibration object and can be considered as 0D approach because only image point correspondences are required. A prototypical paper introduces the widespread application of camera calibration in robot navigation, three-dimensional reconstruction, bio-medicine, virtual reality and visual surveillance [190]. The paper summarizes the methods in different applications, such as traditional calibration, self-calibration and active vision calibration.…”

Section: What Should a Vision System In Gmrs Be Used For?mentioning

confidence: 99%

A Taxonomy of Vision Systems for Ground Mobile Robots

Gómez

Fernández-Caballero

García-Varea

et al. 2014

International Journal of Advanced Robotic Systems

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This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

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“…Trials were regularly organized, offering challenging environment in which the robot had to find its way to reach the goal and learn from its experiments. Thus, the different teams involved in the project, such as Bajracharya, Howard, Matthies, Tang, andTurmon (2009), Huang, Ollis, Happold, andStancil (2009), Kim, Sun, Min Oh, Rehg, and Bobick (2006), Konolige et al (2009), Otte, Richardson, Mulligan, and, Procopio, , and Sermanet et al (2009), showed solutions to enable a robot to learn from its experiment and handle unexpected events. Nevertheless, this research is focused on giving an answer to whether the terrain is traversable.…”

Section: State Of the Artmentioning

confidence: 99%

Adaptive rover behavior based on online empirical evaluation: Rover–terrain interaction and near‐to‐far learning

Krebs

Pradalier

Siegwart

2009

Journal of Field Robotics

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Owing to the fundamental nature of all-terrain exploration, autonomous rovers are confronted with unknown environments. This is especially apparent regarding soil interactions, as the nature of the soil is typically unknown. This work aims at establishing a framework from which the rover can learn from its interaction with the terrains encountered and shows the importance of such a method. We introduce a set of rover-terrain interaction (RTI) and remote data metrics that are expressed in different subspaces. In practice, the information characterizing the terrains, obtained from remote sensors (e.g., a camera) and local sensors (e.g., an inertial measurement unit) is used to characterize the respective remote data and RTI model. In each subspace, which can be described as a feature space encompassing either a remote data measurement or an RTI, similar features are grouped to form classes, and the probability distribution function over the features is learned for each one of those classes. Subsequently, data acquired on the same terrain are used to associate the corresponding models in each subspace and to build an inference model. Based on the remote sensor data measured, the RTI model is predicted using the inference model. This process corresponds to a near-to-far approach and provides the most probable RTI metrics of the terrain lying ahead of the rover. The predicted RTI metrics are then used to plan an optimal path with respect to the RTI model and therefore influence the rover trajectory. The CRAB rover is used in this work for the implementation and testing of the approach, which we call rover-terrain interactions learned from experiments (RTILE). This article presents RTILE, describes its implementation, and concludes with results from field tests that validate the approach. C 2009 Wiley Periodicals, Inc.• First, the performance depends on the robot's physical and mechanical properties, corresponding to its structure, suspension mechanism, actuators, and sensors.• Second, the performance is related to the control of the robotic platform, in a very generic sense. This includes research in fields such as control, obstacle avoidance, path planning, pose estimation, and so forth.As mentioned in the latter point, the robot's performance is related to the interaction of the robot with its surroundings and its capability to sense and represent the environment. A natural environment, which is usually the operating place for all-terrain rovers, involves a great diversity in terrain, soil and obstacle types, shapes, and appearances. This diversity is difficult to model and hence implies additional uncertainty that the rover must cope with.

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Image‐based path planning for outdoor mobile robots

Cited by 11 publications

References 23 publications

Visual road following using intrinsic images

Visual road following using intrinsic images

A Taxonomy of Vision Systems for Ground Mobile Robots

Adaptive rover behavior based on online empirical evaluation: Rover–terrain interaction and near‐to‐far learning

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