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：自主机器人在动态未知环境中的导航对于移动机器人应用至关重要，包括最后一公里配送中的机器人导航以及工业与医院配送中的机器人自动化供应。当前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，用于在密集障碍物和密集人群环境中的自主机器人感知与导航。首先，我们提出了一种框架，能够以零样本方式准确检测并分割开放世界物体类别，这克服了当前SAM模型过度分割的限制。其次，我们提出了蒸馏策略，无需标签即可将知识蒸馏为步行道自由空间的分割以支持机器人导航。同时，我们设计了与蒸馏协同工作的修剪策略，以实现轻量级推理，从而在边缘设备（如NVIDIA-TX2或Xavier NX）上部署神经网络进行自主导航。集成到机器人导航系统中后，大量实验表明，我们提出的框架在机器人场景感知与自主机器人导航的精度和效率方面均取得了优越性能。