PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realistically simulated environments, under the assumptions with noiseless actuation and most importantly, perfect localization with GPS and compass sensors. However, accurate GPS signalis difficult to be obtained in real indoor environment. To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner. Sepecifically, unsupervised VO computes the relative pose of the agent from the re-projection error of two adjacent frames, and then replaces the accurate GPS signal with the path integration. The pseudo position estimated by VO is used to train action integration which assists agent to update their internal perception of location and helps improve the success rate of navigation. The training and inference process only use RGB, depth, collision as well as self-action information. The experiments show that the proposed system achieves satisfactory results and outperforms the partially supervised learning algorithms on the popular Gibson dataset.

翻译：点目标导航是个人机器人在室内环境中导航至指定位置的基础任务。近期研究在照片级真实模拟环境中，基于无噪声执行机构且最关键的是依赖GPS和磁力计传感器实现完美定位的假设下，以近乎完美的成功率解决了该任务。然而，真实室内环境中难以获取精确的GPS信号。为提升无GPS信号条件下的点目标导航精度，我们采用视觉里程计（VO）并提出了一种以无监督方式训练的新型动作集成模块（AIM）。具体而言，无监督VO通过两相邻帧的重投影误差计算智能体的相对位姿，进而以路径积分替代精确的GPS信号。由VO估计的伪位置被用于训练动作集成模块，辅助智能体更新其内部位置感知，从而提升导航成功率。训练与推理过程仅使用RGB图像、深度信息、碰撞检测及自运动信息。实验表明，所提系统在Gibson数据集上取得了满意效果，性能优于部分监督学习算法。