为了对现有机器人的物体识别进行优化和改进,提出了一种新的权重计算方法进行室内场景图像识别。 该方法通过对输入场景的转换获取无向带权图,在表面法方向的基础上,使用表面凹凸度这一指标来 进行表面粗糙度综合判定,取代了传统的布尔判定,大大提升了抗噪性能,避免出现错误传递放大的 情况。基于快速图像分割算法及时识别未知物体。实验结果表明:提出方法的鲁棒性及抗噪能力均较 强,优于单纯基于法方向的方法。与基于深度学习与推测的同类方法相比,所提方法性能更好,更适用于实际识别。

In order to optimize and improve object recognition of the existing robots, a new weight calculation method for interior scene images identification is proposed. The undirected weighted graph is obtained by converting the input scene. Based on surface normal direction, comprehensive determination of surface roughness is carried out using the concave and convex degree index instead of the traditional Boolean decision, which greatly enhances the anti-noise performance and avoids the error propagation amplification. The unknown objects are identified in time based on fast image segmentation algorithm. Experiment results show that the robustness and anti-noise ability of the proposed method are both strong, and the proposed method is better than methods based on normal direction only. Compared with other methods based on deep learning and conjecture, the proposed method has better performance, and it’s more applicable to the practical identification.