  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.
