When mobile robots navigate autonomously in multi-storey buildings,they need to pos-sess the capability of staircase detection and modeling.However,the current staircase detection and modeling methods are mainly for single-storey straight staircase,which have limited applica-bility to multi-storey staircase with rotational structures.Moreover,the presence of dynamic point clouds in the scene affects the modeling accuracy.A multi-storey staircase detection and modeling method based on LiDAR is proposed.Firstly,a lightweight 2.5D point cloud filter is constructed to efficiently remove dynamic and noisy point clouds.Secondly,a step classification method based on principal component analysis is introduced to achieve robust step detection for single and com-plex multi-storey staircase with rotational structures.Additionally,a region-based segmentation approach is designed for estimating step dimensions,aiming at improving the accuracy of staircase model construction.Furthermore,a staircase model tracking method based on multiple threshold judgments is proposed to enable real-time staircase detection and fast dynamic modeling.Finally,experiments are conducted to validate the proposed method,using the Tang dataset and point cloud data collected from a quadruped robot in various indoor and outdoor multiple scenarios.The results show that the proposed method is capable of dynamically detecting and modeling both sin-gle-storey and multi-storey staircase with rotational structures in indoor and outdoor scenarios.The estimated accuracy of step dimensions is within 1 cm,and there is a 51.23％reduction in com-putational complexity.
Staircase detection and modelingPrincipal component analysisLiDARQuadruped robot climbing staircaseDynamic scenes
NETL
NSTL
万方数据
