A new tentacles-based technique for avoiding obstacles during visual navigation

Cherubini, Andrea; Spindler, Fabien; Chaumette, François

doi:10.1109/icra.2012.6224584

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…Indeed, if such deviation is not encountered during training, the robot will have no memory of what it sees after deviating. Recent improvements by Cherubini, Spindler, and Chaumette () have shown promising results in overcoming this difficulty.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

2014

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While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle-friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules-place recognition, landmark recognition, and road lane detectionsupplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid-occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real-time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route. C 2014 Wiley Periodicals, Inc.

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Section: Related Workmentioning

confidence: 99%

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

2014

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“…In contrast with those works, our approach does not require the robot pose, and exploits the robot geometric and kinematic characteristics (this aspect will be detailed later in the paper). A detailed comparison between the potential field and the tentacle techniques is given in [Cherubini et al 2012]. In that work, we showed that with tentacles, smoother control inputs are generated, higher velocities can be applied, and only dangerous obstacles are taken into account.…”

Section: Introductionmentioning

confidence: 98%

Visual navigation of a mobile robot with laser-based collision avoidance

Cherubini

Chaumette

2012

The International Journal of Robotics Research

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International audienceIn this paper, we propose and validate a framework for visual navigation with collision avoidance for a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such path, the robot is able to avoid obstacles which were not present during teaching, and which are sensed by an on-board range scanner. Our control scheme guarantees that obstacle avoidance and navigation are achieved simultaneously. In fact, in the presence of obstacles, the camera pan angle is actuated to maintain scene visibility while the robot circumnavigates the obstacle. The risk of collision and the eventual avoiding behaviour are determined using a tentacle-based approach. The framework can also deal with unavoidable obstacles, which make the robot decelerate and eventually stop. Simulated and real experiments show that with our method, the vehicle can navigate along a visual path while avoiding collisions

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“…The success of the technique depends on being able to quickly and robustly match the input visual features despite changes in lighting conditions or in the presence of dynamic obstacles. Another concern is how to get back to the set route when the robot has to deviate from it momentarily while avoiding novel obstacles (pedestrians), although recent improvements [7] have shown promising results.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition

Siagian

Chang

Itti

2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-We present a mobile robot navigation system guided by a novel vision-based road recognition approach. The system represents the road as a set of lines extrapolated from the detected image contour segments. These lines enable the robot to maintain its heading by centering the vanishing point in its field of view, and to correct the long term drift from its original lateral position. We integrate odometry and our visual road recognition system into a grid-based local map that estimates the robot pose as well as its surroundings to generate a movement path. Our road recognition system is able to estimate the road center on a standard dataset with 25,076 images to within 11.42 cm (with respect to roads at least 3 m wide). It outperforms three other state-of-the-art systems. In addition, we extensively test our navigation system in four busy college campus environments using a wheeled robot. Our tests cover more than 5 km of autonomous driving without failure. This demonstrates robustness of the proposed approach against challenges that include occlusion by pedestrians, non-standard complex road markings and shapes, shadows, and miscellaneous obstacle objects.

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A new tentacles-based technique for avoiding obstacles during visual navigation

Cited by 24 publications

References 18 publications

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

Visual navigation of a mobile robot with laser-based collision avoidance

Mobile robot navigation system in outdoor pedestrian environment using vision-based road recognition

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