@article { Wes2018,
     author        = { Thomas Westfechtel and Kazunori Ohno and B{"a}rbel Mertsching and Ryunosuke Hamada and Daniel Nickchen and Shotaro Kojima and Satoshi Tadokoro },
     title         = { Robust Stairway Detection and Localization Method for Mobile Robots using a Graph-Based Model and Competing Initializations },
     month         = { July },
     year          = { 2018 },
     journal       = { International Journal of Robotics  Research },
     number        = { 12 },
     pages         = { 1463 -- 1483 },
     volume        = { 37 },
     abstract      = { 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 2.5mm 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. }
}