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机器人导航以及工业与医院配送场景中机器人自动化物资供应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，用于机器人在密集障碍物与密集人流环境中的自主感知与导航。首先，我们提出了一种以零样本方式准确检测与分割开放世界物体类别的框架，克服了当前SAM模型过度分割的局限性。其次，我们设计了蒸馏策略，将知识蒸馏至用于机器人导航的无标签走道自由空间分割中。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，从而在自主导航过程中将神经网络部署于NVIDIA-TX2或Xavier NX等边缘设备上。集成至机器人导航系统后，大量实验表明，我们提出的框架在机器人场景感知与自主导航的准确性与效率方面均取得了卓越性能。