HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems
This work addresses the bandwidth-accuracy trade-off in collaborative perception for multi-robot systems, offering a practical solution for bandwidth-constrained real-world deployments.
HydraCollab proposes an adaptive collaborative-perception framework that selectively transmits informative features and dynamically switches between intermediate and late collaboration strategies to balance communication bandwidth and perception accuracy. On V2X-R and V2X-Radar datasets, it uses only 41% and 26% of the bandwidth of SOTA Where2comm while improving performance by 0.78% and 0.75% respectively.
Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.