A consistent spatial-temporal coordination across multiple agents is
fundamental for collaborative perception, which seeks to improve perception
abilities through information exchange among agents. To achieve this
spatial-temporal alignment, traditional methods depend on external devices to
provide localization and clock signals. However, hardware-generated signals
could be vulnerable to noise and potentially malicious attack, jeopardizing the
precision of spatial-temporal alignment. Rather than relying on external
hardwares, this work proposes a novel approach: aligning by recognizing the
inherent geometric patterns within the perceptual data of various agents.
Following this spirit, we propose a robust collaborative perception system that
operates independently of external localization and clock devices. The key
module of our system,~\emph{FreeAlign}, constructs a salient object graph for
each agent based on its detected boxes and uses a graph neural network to
identify common subgraphs between agents, leading to accurate relative pose and
time. We validate \emph{FreeAlign} on both real-world and simulated datasets.
The results show that, the ~\emph{FreeAlign} empowered robust collaborative
perception system perform comparably to systems relying on precise localization
and clock devices.

通过识别各个代理的感知数据中固有的几何图案来实现空间时域能力的协调，提出了一种不依赖外部定位和时钟设备的强大的协同感知系统，其中关键模块为 FreeAlign，通过构建每个代理的显著对象图，并使用图神经网络来识别代理之间的共同子图，从而获得准确的相对姿势和时间。验证结果表明，FreeAlign 增强的鲁棒协同感知系统性能与依赖精确定位和时钟设备的系统相当。