NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation

[2406.13086] NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2406.13086 (cs) [Submitted on 18 Jun 2024 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation Authors: Timothy K Johnsen , Ian Harshbarger , Zixia Xia , Marco Levorato View a PDF of the paper titled NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation, by Timothy K Johnsen and 3 other authors View PDF HTML (experimental) Abstract: 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. Comments: 6 pages, 3 figures Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2406.13086 [cs.RO] (or arXiv:2406.13086v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2406.13086 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Related DOI : https://doi.org/10.1109/WoWMoM60985.2024.00041 Focus to learn more DOI(s) linking to related resources Submission history From: Tim Johnsen [ view email ] [v1] Tue, 18 Jun 2024 22:25:09 UTC (1,318 KB) [v2] Wed, 8 Apr 2026 18:23:59 UTC (585 KB) Full-text links: Access Paper: View a PDF of the paper titled NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation, by Timothy K Johnsen and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2024-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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