Abstract Mobile service robots possess high potential of providing numerous assistances in the working areas. In an attempt to develop a mobile service robot which is dynamically balanced for faster movement and taller manipulation capability, we designed and prototyped J4.alpha, which is intended for swift navigation and nimble manipulation. Previously, we devised a pure visual method based on a supervised deep learning model for real-time recognition of nodal locations. Four low-resolution RGB cameras are installed around J4.alpha to capture the surrounding visual features for training and detection. As the method is developed for ease of implementation, fast real-time application, accurate detection, and low cost, we further improve the accuracy and the practicality of the method in this study. Specifically, a set of expectation rules are introduced to reject outlier detections, and a scheme of training renewal is devised to effectively react to environmental modifications. In our previous tests, precision and recall rates of the location coordinate detection by the ConvNet models were generally between 0.78 and 0.91; by introducing the expectation rules, precision and recall are improved by approximately 10%. A large scale field test is also carried out here for both corridor and factory scenarios; the performance of the proposed method was tested for detection accuracy and verified for 2 m and 0.5 m nodal intervals. The scheme of training renewal designed for capturing and reflecting environmental modifications was also proved to be effective.

中文翻译：

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使用具有期望规则和训练更新的结构化视图 ConvNet 进行实时拓扑定位

摘要 移动服务机器人具有在工作领域提供众多帮助​​的巨大潜力。为了开发动态平衡以实现更快移动和更高操纵能力的移动服务机器人，我们设计并制作了 J4.alpha 原型，旨在实现快速导航和灵活操纵。之前，我们设计了一种基于监督深度学习模型的纯视觉方法，用于实时识别节点位置。J4.alpha 周围安装了四个低分辨率 RGB 摄像头，用于捕捉周围的视觉特征，用于训练和检测。由于该方法易于实施，实时应用快，检测准确，成本低，我们进一步提高了本研究方法的准确性和实用性。具体来说，引入了一组期望规则来拒绝异常检测，并设计了训练更新方案以有效应对环境变化。在我们之前的测试中，ConvNet 模型对位置坐标检测的准确率和召回率一般在 0.78 到 0.91 之间；通过引入期望规则，准确率和召回率提高了大约 10%。这里还针对走廊和工厂场景进行了大规模的现场测试；对所提出方法的性能进行了检测精度测试，并在 2 m 和 0.5 m 节点间隔上进行了验证。为捕捉和反映环境变化而设计的培训更新计划也被证明是有效的。并制定了培训更新计划，以有效应对环境变化。在我们之前的测试中，ConvNet 模型对位置坐标检测的准确率和召回率一般在 0.78 到 0.91 之间；通过引入期望规则，准确率和召回率提高了大约 10%。这里还针对走廊和工厂场景进行了大规模的现场测试；对所提出方法的性能进行了检测精度测试，并在 2 m 和 0.5 m 节点间隔上进行了验证。为捕捉和反映环境变化而设计的培训更新计划也被证明是有效的。并制定了培训更新计划，以有效应对环境变化。在我们之前的测试中，ConvNet 模型对位置坐标检测的准确率和召回率一般在 0.78 到 0.91 之间；通过引入期望规则，准确率和召回率提高了大约 10%。这里还针对走廊和工厂场景进行了大规模的现场测试；对所提出方法的性能进行了检测精度测试，并在 2 m 和 0.5 m 节点间隔上进行了验证。为捕捉和反映环境变化而设计的培训更新计划也被证明是有效的。通过引入期望规则，准确率和召回率提高了大约 10%。这里还针对走廊和工厂场景进行了大规模的现场测试；对所提出方法的性能进行了检测精度测试，并在 2 m 和 0.5 m 节点间隔上进行了验证。为捕捉和反映环境变化而设计的培训更新计划也被证明是有效的。通过引入期望规则，准确率和召回率提高了大约 10%。这里还针对走廊和工厂场景进行了大规模的现场测试；对所提出方法的性能进行了检测精度测试，并在 2 m 和 0.5 m 节点间隔上进行了验证。为捕捉和反映环境变化而设计的培训更新计划也被证明是有效的。