Descending-stair detection, approach, and traversal with an autonomous tracked vehicle

Hesch, Joel A.; Mariottini, Gian Luca; Roumeliotis, Stergios I.

doi:10.1109/iros.2010.5649411

Cited by 43 publications

(13 citation statements)

References 12 publications

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“…For example, the stair climbing behavior can be endowed with descent capability (as in [30] via [31]), as well more deliberative obstacle avoidance (as in [32]). The hill climbing behavior could use grade to cue a greater diversity of better hill climbing gaits [33] in order to climb hills as steep as 45…”

Section: Discussionmentioning

confidence: 99%

Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator. Abstract -This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator.

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Section: Discussionmentioning

confidence: 99%

Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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“…However, these approaches also assume that the robot is parallel to the staircase and facing it. Some other sensors that have been used are monocular [14][15][16][17] and stereo sensors [18].…”

Section: Related Workmentioning

confidence: 99%

Staircase Recognition and Localization Using Convolution Neural Network (CNN) for Cleaning Robot Application

Ilyas¹,

Ak²,

Av³

et al. 2018

Preprint

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Multi-floor environments are usually ignored while designing an autonomous robot for indoor cleaning applications. However, for efficient operation in such environments, the ability of a robotic platform to traverse staircases is crucial. Staircase detection and localization is highly important for planning the traversal on staircases. This paper describes a deep learning approach using Convolutional Neural Networks (CNNs) based Robot Operation System (ROS) to staircase detection and localization. We use an object detection network to detect staircases in images. We also localize these staircases using a contour detection algorithm to detect the target point, a point close to the center of the first step, and the angle of approach to the target point. Experiments are performed with data obtained from images captured on different types of staircases at different view points/angles. Results show that the approach is very accurate in identifying the presence of the staircase in the working environment and is also able to locate the target point with good accuracy.

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“…In the same vein, Hernandez et al [16] propose to find the diagonal lines corresponding to the handrails to select stair candidates. Hesch et al [17] focus on detecting descending staircases for small ground robots in both far and near distance. Stairs are usually characterized by a distinctive shape formed by a flight of steps, which makes the sensors capable of measure depth the most appropriate for the task, as they provide richer information.…”

Section: Related Workmentioning

confidence: 99%

Stairs detection with odometry-aided traversal from a wearable RGB-D camera

Pérez-Yus

Gutiérrez-Gómez

López‐Nicolás

et al. 2017

Computer Vision and Image Understanding

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Stairs are one of the most common structures present in human-made scenarios, but also one of the most dangerous for those with vision problems. In this work we propose a complete method to detect, locate and parametrise stairs with a wearable RGB-D camera. Our algorithm uses the depth data to determine if the horizontal planes in the scene are valid steps of a staircase judging their dimensions and relative positions. As a result we obtain a scaled model of the staircase with the spatial location and orientation with respect to the subject. The visual odometry is also estimated to continuously recover the current position and orientation of the user while moving. This enhances the system giving the ability to come back to previously detected features and providing location awareness of the user during the climb. Simultaneously, the detection of the staircase during the traversal is used to correct the drift of the visual odometry. A comparison of results of the stair detection with other state-of-the-art algorithms was performed using public dataset. Additional experiments have also been carried out, recording our own natural scenes with a chest-mounted RGB-D camera in indoor scenarios. The algorithm is robust enough to work in real-time and even under partial occlusions of the stair.

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Descending-stair detection, approach, and traversal with an autonomous tracked vehicle

Cited by 43 publications

References 12 publications

Autonomous legged hill and stairwell ascent

Autonomous legged hill and stairwell ascent

Staircase Recognition and Localization Using Convolution Neural Network (CNN) for Cleaning Robot Application

Stairs detection with odometry-aided traversal from a wearable RGB-D camera

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