Autonomous robot navigation within the dynamic unknown environment is of crucial significance for mobile robotic applications including robot navigation in last-mile delivery and robot-enabled automated supplies in industrial and hospital delivery applications. Current solutions still suffer from limitations, such as the robot cannot recognize unknown objects in real time and cannot navigate freely in a dynamic, narrow, and complex environment. We propose a complete software framework for autonomous robot perception and navigation within very dense obstacles and dense human crowds. First, we propose a framework that accurately detects and segments open-world object categories in a zero-shot manner, which overcomes the over-segmentation limitation of the current SAM model. Second, we proposed the distillation strategy to distill the knowledge to segment the free space of the walkway for robot navigation without the label. In the meantime, we design the trimming strategy that works collaboratively with distillation to enable lightweight inference to deploy the neural network on edge devices such as NVIDIA-TX2 or Xavier NX during autonomous navigation. Integrated into the robot navigation system, extensive experiments demonstrate that our proposed framework has achieved superior performance in terms of both accuracy and efficiency in robot scene perception and autonomous robot navigation.

翻译：在动态未知环境中实现自主机器人导航，对包括最后一公里配送中的机器人导航、工业与医院物流中的自动化物资运输等移动机器人应用具有关键意义。现有解决方案仍存在局限性，例如机器人无法实时识别未知物体，且无法在动态、狭窄且复杂的环境中自由导航。我们提出了一套完整的软件框架，用于机器人在高密度障碍物与密集人群环境中的自主感知与导航。首先，我们提出了一种框架，能以零样本方式准确检测并分割开放世界物体类别，克服了当前SAM模型过分割的局限。其次，我们设计了蒸馏策略，在无需标注的情况下将知识蒸馏至可分割机器人导航通道自由空间的过程。同时，我们设计了与蒸馏协同工作的修剪策略，实现轻量级推理，使神经网络能在自主导航过程中部署于NVIDIA-TX2或Xavier NX等边缘设备。将所提框架集成至机器人导航系统后，大量实验表明：在机器人场景感知与自主导航的精度与效率方面，该框架均取得了优越性能。