Staircase Localization for Autonomous Exploration in Urban Environments
Jinrae Kim Sunggoo Jung Sung-Kyun Kim Youdan Kim Ali-akbar Agha-mohammadi
A staircase localization method is proposed for robots to explore urban
environments autonomously. The proposed method employs a modular design in the
form of a cascade pipeline consisting of three modules of stair detection, line
segment detection, and stair localization modules. The stair detection module
utilizes an object detection algorithm based on deep learning to generate a
region of interest (ROI). From the ROI, line segment features are extracted
using a deep line segment detection algorithm. The extracted line segments are
used to localize a staircase in terms of position, orientation, and stair
direction. The stair detection and localization are performed only with a
single RGB-D camera. Each component of the proposed pipeline does not need to
be designed particularly for staircases, which makes it easy to maintain the
whole pipeline and replace each component with state-of-the-art deep learning
detection techniques. The results of real-world experiments show that the
proposed method can perform accurate stair detection and localization during
autonomous exploration for various structured and unstructured upstairs and
downstairs with shadows, dirt, and occlusions by artificial and natural
objects.