Despite the recent success of state-of-the-art 3D object recognition approaches, service robots still frequently fail to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unpredictable environmental conditions. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Furthermore, they mainly utilize the \(L_n\) Minkowski family functions to measure the similarity of two object views, while there are various distance measures that are applicable to compare two object views. In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition. Toward this goal, we extensively evaluate the performance of object recognition approaches in three different configurations, including color-only, shape-only, and combinations of color and shape, in both offline and online settings. Experimental results concerning scalability, memory usage, and object recognition performance show that all of the combinations of color and shape yield significant improvements over the shape-only and color-only approaches. The underlying reason is that color information is an important feature to distinguish objects that have very similar geometric properties with different colors and vice versa. Moreover, by combining color and shape information, we demonstrate that the robot can learn new object categories from very few training examples in a real-world setting.

尽管最新的3D对象识别方法取得了成功，但服务机器人仍然经常无法在以人为中心的真实环境中识别许多对象。对于这些机器人，由于在变化莫测的环境条件下对准确和实时响应的需求很高，因此物体识别是一项艰巨的任务。最近的大多数方法仅使用形状信息，而忽略颜色信息的作用，反之亦然。此外，他们主要利用\（L_n \）Minkowski系列功能可测量两个对象视图的相似性，同时有各种距离度量可用于比较两个对象视图。在本文中，我们探讨了形状信息，颜色恒定性，颜色空间以及各种相似性度量在开放式3D对象识别中的重要性。为了实现这一目标，我们广泛的评估对象识别的性能三种不同的配置，其中包括接近的颜色只，形只，和颜色和形状的组合，在离线和在线设置。关于可伸缩性，内存使用和对象识别性能的实验结果表明，颜色和形状的所有组合收率显著改进在形状只和色彩只接近。根本原因在于，颜色信息是区分具有非常相似的几何特性（具有不同颜色）的对象的重要特征，反之亦然。此外，通过组合颜色和形状信息，我们证明了机器人可以在现实环境中从很少的训练示例中学习新的对象类别。