Visual navigation with a time-independent varying reference

Cherubini, Andrea; Chaumette, François

doi:10.1109/iros.2009.5354284

Cited by 7 publications

(2 citation statements)

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“…The controller presented in [11] exploits angular information regarding the features matched in panoramic images. In [12], a novel varying reference based teach-and-repeat framework is proposed for outdoor navigation with a monocular camera.…”

Section: Related Workmentioning

confidence: 99%

Lidar-based teach-and-repeat of mobile robot trajectories

Sprunk

Tipaldi

Cherubini

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Automation of logistics tasks for small lot sizes and flexible production processes requires intuitive and easy-to-use systems that allow non-expert shop floor workers to naturally instruct transportation systems. To this end, we present a novel laser-based scheme for teach-and-repeat of mobile robot trajectories that relies on scan matching to localize the robot relative to a taught trajectory, which is represented by a sequence of raw odometry and 2D laser data. This approach has two advantages. First, it does not require to build a globally consistent metrical map of the environment, which reduces setup time. Second, the direct use of raw sensor data avoids additional errors that might be introduced by the fact that grid maps only provide an approximation of the environment. Real-world experiments carried out with a holonomic and a differential drive platform demonstrate that our approach repeats trajectories with an accuracy of a few millimeters. A comparison with a standard Monte Carlo localization approach on grid maps furthermore reveals that our method yields lower tracking errors for teach-and-repeat tasks.

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Section: Related Workmentioning

confidence: 99%

Lidar-based teach-and-repeat of mobile robot trajectories

Sprunk

Tipaldi

Cherubini

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…On the other hand, local or reactive strategies are based on instantaneous information from on-board sensors. These strategies include potential fields, 4,5 vector field histogram, 6 dynamic window, 7 obstacle-restriction method, 8 and closest gap. 9 In contrast with static environments, where classical path planning approaches are suitable, dynamic environments are more challenging to deal with.…”

Section: Introductionmentioning

confidence: 99%

Sensor-based Navigation of Omnidirectional Wheeled Robots Dealing with both Collisions and Occlusions

2019

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SUMMARYNavigation tasks are often subject to several constraints that can be related to the sensors (visibility) or come from the environment (obstacles). In this paper, we propose a framework for autonomous omnidirectional wheeled robots that takes into account both collision and occlusion risk, during sensor-based navigation. The task consists in driving the robot towards a visual target in the presence of static and moving obstacles. The target is acquired by fixed – limited field of view – on-board cameras, while the surrounding obstacles are detected by lidar scanners. To perform the task, the robot has not only to keep the target in view while avoiding the obstacles, but also to predict its location in the case of occlusion. The effectiveness of our approach is validated through several experiments.

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