Two common methods exist for solving indoor autonomous navigation and obstacle-avoidance problems using monocular vision: the traditional simultaneous localization and mapping (SLAM) method, which requires complex hardware, heavy calculations, and is prone to errors in low texture or dynamic environments; and deep-learning algorithms, which use the fully connected layer for classification or regression, resulting in more model parameters and easy over-fitting. Among the latter ones, the most advanced indoor navigation algorithm divides a single image frame into multiple parts for prediction, resulting in doubled reasoning time. To solve these problems, we propose a multi-task deep network based on feature map region division for monocular indoor autonomous navigation. We divide the feature map instead of the original image to avoid repeated information processing. To reduce model parameters, we use convolution instead of the fully connected layer to predict the navigable probability of the left, middle, and right parts. We propose that the linear velocity is determined by combining three prediction probabilities to reduce collision risk. Experimental evaluation shows that the proposed method is nine times smaller than the previous state-of-the-art methods; further, its processing speed and navigation capability increase more than five and 1.6 times, respectively.

中文翻译：

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用于自主室内无人机导航的基于部分的多任务深度网络

使用单目视觉解决室内自主导航和避障问题的常用方法有两种：传统的同时定位和建图（SLAM）方法，硬件复杂，计算量大，在低纹理或动态环境中容易出错；深度学习算法，使用全连接层进行分类或回归，导致模型参数更多，容易过拟合。在后者中，最先进的室内导航算法将单个图像帧分成多个部分进行预测，导致推理时间加倍。为了解决这些问题，我们提出了一种基于特征图区域划分的多任务深度网络，用于单目室内自主导航。我们划分特征图而不是原始图像以避免重复信息处理。为了减少模型参数，我们使用卷积代替全连接层来预测左、中、右部分的可导航概率。我们建议通过组合三个预测概率来确定线速度以降低碰撞风险。实验评估表明，所提出的方法比以前最先进的方法小九倍；此外，其处理速度和导航能力分别提高了 5 倍和 1.6 倍以上。我们建议通过组合三个预测概率来确定线速度以降低碰撞风险。实验评估表明，所提出的方法比以前最先进的方法小九倍；此外，其处理速度和导航能力分别提高了 5 倍和 1.6 倍以上。我们建议通过组合三个预测概率来确定线速度，以降低碰撞风险。实验评估表明，所提出的方法比以前最先进的方法小九倍；此外，其处理速度和导航能力分别提高了 5 倍和 1.6 倍以上。