NaviSplit：动态多分支分割DNNs用于高效分布式自主导航
NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation
AI总结：
本文提出NaviSplit框架，通过动态多分支DNN实现高效自主导航，减少数据传输量并提升导航精度。
摘要：
轻量级自主无人机正成为多种应用的核心组件。然而，自主导航需要实现感知算法，如深度神经网络（DNN），以处理传感器观测输入（如摄像头和LiDAR的数据）以生成控制逻辑。此类算法的复杂性与设备在计算能力、能量、内存和执行时间方面的严格限制相冲突。本文提出NaviSplit，这是首个轻量级导航框架，嵌入了分布式和动态多分支神经模型。其核心是将DNN在压缩点分割，产生两个模型部分：（1）头部模型在车辆上执行，部分处理并压缩传感器感知数据；（2）尾部模型在互联的计算设备上执行，处理剩余压缩后的感知数据并推断导航指令。与先前工作不同，NaviSplit框架包含一个神经门，动态选择特定的头部模型以最小化通道使用并高效支持导航网络。在我们的实现中，感知模型使用Microsoft AirSim稳健模拟器从无人机捕获的单目RGB图像中提取2D深度图。我们的结果表明，NaviSplit深度模型在提取精度上达到72-81%，同时向边缘服务器传输极小的数据量（1.2-18 KB）。当使用神经门时，我们比使用较大静态网络获得略高的导航精度（0.3%），同时显著降低数据速率（95%）。据我们所知，这是首个基于分割DNN的动态多分支模型用于自主导航的实例。
英文摘要：
Lightweight autonomous unmanned aerial vehicles (UAV) are emerging as a central component of a broad range of applications. However, autonomous navigation necessitates the implementation of perception algorithms, often deep neural networks (DNN), that process the input of sensor observations, such as that from cameras and LiDARs, for control logic. The complexity of such algorithms clashes with the severe constraints of these devices in terms of computing power, energy, memory, and execution time. In this paper, we propose NaviSplit, the first instance of a lightweight navigation framework embedding a distributed and dynamic multi-branched neural model. At its core is a DNN split at a compression point, resulting in two model parts: (1) the head model, that is executed at the vehicle, which partially processes and compacts perception from sensors; and (2) the tail model, that is executed at an interconnected compute-capable device, which processes the remainder of the compacted perception and infers navigation commands. Different from prior work, the NaviSplit framework includes a neural gate that dynamically selects a specific head model to minimize channel usage while efficiently supporting the navigation network. In our implementation, the perception model extracts a 2D depth map from a monocular RGB image captured by the drone using the robust simulator Microsoft AirSim. Our results demonstrate that the NaviSplit depth model achieves an extraction accuracy of 72-81% while transmitting an extremely small amount of data (1.2-18 KB) to the edge server. When using the neural gate, as utilized by NaviSplit, we obtain a slightly higher navigation accuracy as compared to a larger static network by 0.3% while significantly reducing the data rate by 95%. To the best of our knowledge, this is the first exemplar of dynamic multi-branched model based on split DNNs for autonomous navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
