首页|Findings on Robotics and Automation Detailed by Investigators at Carnegie Mellon University (anyloc: Towards Universal Visual Place Recognition)
Findings on Robotics and Automation Detailed by Investigators at Carnegie Mellon University (anyloc: Towards Universal Visual Place Recognition)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
Investigators publish new report on Robotics - Robotics and Automation. According to news originating from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, “Visual Place Recognition (VPR) is vital for robot localization. To date, the most performant VPR approaches are environment- and task-specific: while they exhibit strong performance in structured environments (predominantly urban driving), their performance degrades severely in unstructured environments, rendering most approaches brittle to robust real-world deployment.” Financial support for this research came from US Army Research Laboratory (ARL). Our news journalists obtained a quote from the research from Carnegie Mellon University, “In this work, we develop a universal solution to VPR - a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or finetuning. We demonstrate that general-purpose feature representations derived from off-the-shelf self-supervised models with no VPR-specific training are the right substrate upon which to build such a universal VPR solution. Combining these derived features with unsupervised feature aggregation enables our suite of methods, AnyLoc, to achieve up to 4x significantly higher performance than existing approaches. We further obtain a 6% improvement in performance by characterizing the semantic properties of these features, uncovering unique domains which encapsulate datasets from similar environments.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaRobotics and AutomationRoboticsCarnegie Mellon University
NETL
NSTL
IEEE
