Purpose

The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment.

Design/methodology/approach

The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity.

Findings

In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm.

Originality/value

The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.

中文翻译：

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腿部机器人运动的基于平面的楼梯映射

目的

这项研究的目的是提出一种新的基于平面的映射方法，用于楼梯环境中的腿式机器人导航。

设计/方法/方法

本研究中采用的方法使用传感器测量值和预测值的加权平均值来估算楼梯每一步的飞机。它通过随机样本共识（RANSAC）从点云数据中分割平面。预测使用楼梯的规则结构。在估计平面时，该算法除了考虑阶梯误差之外，还考虑了使用协方差矩阵的距离传感器和RANSAC引入的误差。平面系数与本研究中建议的数据结构分开管理。另外，该数据结构允许算法将每个阶梯的信息存储为单个实体。

发现

在楼梯环境中，所提出的算法所提供的准确性要高于传统映射方法所提供的准确性。硬件实验验证了该算法的准确性和适用性。

创意/价值

所提出的算法提供了准确的楼梯-环境映射以及每个楼梯的详细规格。使用此信息，有腿机器人可以在楼梯环境中更有效地导航和计划其运动。