Robot navigation systems suffer from relatively localizing the robots and object goals in the three-dimensional(3D) dynamic environment. Especially, most object detection algorithms adopt in navigation suffer from large resource consumption and a low calculation rate. Hence, this paper proposes a lightweight PyTorch-based monocular vision 3D aware object goal navigation system for nursing robot, which relies on a novel pose-adaptive algorithm for inverse perspective mapping (IPM) to recover 3D information of an indoor scene from a monocular image. First, it detects objects and combines their location with the bird-eye view (BEV) information from the improved IPM to estimate the objects’ orientation, distance, and dynamic collision risk. Additionally, the 3D aware object goal navigation network utilizes an improved spatial pyramid pooling strategy, which introduces an average-pooling branch and a max-pooling branch, better integrating local and global features and thus improving detection accuracy. Finally, a novel pose-adaptive algorithm for IPM is proposed, which introduces a novel voting mechanism to adaptively compensate for the monocular camera’s pose variations to enhance further the depth information accuracy, called the adaptive IPM algorithm. Several experiments demonstrate that the proposed navigation algorithm has a lower memory consumption, is computationally efficient, and improves ranging accuracy, thus meeting the requirements for autonomous collision-free navigation.

机器人导航系统面临着机器人和物体目标在三维（3D）动态环境中相对定位的问题。尤其是导航中采用的大多数目标检测算法都存在资源消耗大、计算率低的问题。因此，本文提出了一种基于 PyTorch 的轻量级单目视觉 3D 感知目标目标导航系统，用于护理机器人，该系统依靠一种新颖的逆透视映射 (IPM) 姿势自适应算法从单目图像中恢复室内场景的 3D 信息。首先，它检测物体并将其位置与改进的 IPM 中的鸟瞰图 (BEV) 信息相结合，以估计物体的方向、距离和动态碰撞风险。此外，3D感知目标目标导航网络采用改进的空间金字塔池化策略，引入平均池化分支和最大池化分支，更好地集成局部和全局特征，从而提高检测精度。最后，提出了一种新颖的IPM姿态自适应算法，该算法引入了一种新颖的投票机制来自适应补偿单目相机的姿态变化，以进一步提高深度信息的准确性，称为自适应IPM算法。多次实验表明，该导航算法具有较低的内存消耗、计算效率高、测距精度高，满足自主无碰撞导航的要求。