Multisensor integrated stair recognition and parameters measurement system for dynamic stair climbing robots

Luo, Ren C.; Hsiao, Ming; Liu, Che-Wei

doi:10.1109/coase.2013.6654026

Cited by 16 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the human body a polyhedron [16] or a model with 41 degrees of freedom [17], it can be moved by the environment to detect obstacles, the steps, the slope of the ground, and the visibility of the signs. In a similar way to a human body, some humanoid robots designed to climb stairs also integrate 3D data capture sensors and software to recognize stairs and calculate the movement needed to climb them [18,19].…”

Section: Related Workmentioning

confidence: 99%

Accessible Routes Integrating Data from Multiple Sources

Luaces

Fisteus

Sánchez-Fernandez

et al. 2020

IJGI

View full text Add to dashboard Cite

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

show abstract

Section: Related Workmentioning

confidence: 99%

Accessible Routes Integrating Data from Multiple Sources

Luaces

Fisteus

Sánchez-Fernandez

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

“…Most literature involving addressing stairs and ramps detection is related to indoor environments. Oßwald et al [30] and Luo et al [31] detect vertical and horizontal planes that forming stairs and extract their geometric parameters. Both papers use humanoid robots climbing and scanning stairs.…”

Section: Urban Element Classificationmentioning

confidence: 99%

Automatic classification of urban ground elements from mobile laser scanning data

Balado

Díaz-Vilariño

Arias

et al. 2018

Automation in Construction

View full text Add to dashboard Cite

Accessibility diagnosis of as-built urban environments is essential for path planning, especially in case of people with reduced mobility and it requires an in-depth knowledge of ground elements. In this paper, we present a new approach for automatically detect and classify urban ground elements from 3D point clouds. The methodology enables a high level of detail classification from the combination of geometric and topological information. The method starts by a planar segmentation followed by a refinement based on split and merge operations. Next, a feature analysis and a geometric decision tree are followed to classify regions in preliminary classes. Finally, adjacency is studied to verify and correct the preliminary classification based on a comparison with a topological graph library. The methodology is tested in four real complex case studies acquired with a Mobile Laser Scanner Device. In total, five classes are considered (roads, sidewalks, treads, risers and curbs). Results show a success rate of 97% in point classification, enough to analyse extensive urban areas from an accessibility point of view. The combination of topology and geometry improves a 10% to 20% the success rate obtained with only the use of geometry.

show abstract

“…Although the publication focused on table-top scenes and did not address stairways, it is only reasonable to assume that it could easily be extended to stairways, given additional training data for the support vector machine process. Luo et al (2013) used a vertical histogram to detect horizontal planes. Equidistant stair treads were used to initialize an adjustable stairway model.…”

Section: Stairway Detectionmentioning

confidence: 99%

Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

Westfechtel

Ohno

Mertsching

et al. 2018

The International Journal of Robotics Research

View full text Add to dashboard Cite

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only 2:5 mm for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small-or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system's accuracy is higher than most state-of-the-art stairway-detection methods.

show abstract

Multisensor integrated stair recognition and parameters measurement system for dynamic stair climbing robots

Cited by 16 publications

References 14 publications

Accessible Routes Integrating Data from Multiple Sources

Accessible Routes Integrating Data from Multiple Sources

Automatic classification of urban ground elements from mobile laser scanning data

Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

Contact Info

Product

Resources

About