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Announcing FPV-SLAM, a state-of-the-art SLAM system for egocentric data. FPV-SLAM ranks 1st in the monocular-inertial track of LaMAria, ETH Zurich's city-scale benchmark.
13,647 views • 12 days ago •via X (Twitter)
30 Comments
Read the full blog here:
@fpv_labs Leading across all categories of short, med, long, low light, moving platform etc.
@pratyush21225 @fpv_labs Congrats!!! No. 1 in LaMAria is crazy 🚀🚀
@pratyush21225 @fpv_labs Thank you sir!
@fpv_labs Congrats on the lead! Looking forward to reading about it (and improving Mighty Camera) 😬
@fpv_labs Congratulations to the whole team! 🌟🔥
@fpv_labs Thanks - we've come a long way since we published our first blog on the challenges with egocentric SLAM
@fpv_labs LFG!!!🚀🚀🚀🚀
@fpv_labs LFG!
@fpv_labs congratulations 🔥
@fpv_labs Thanks buddy!
@fpv_labs Congratulations!
@fpv_labs Thank you sir!
@fpv_labs congrats on the launch! lfgg
@fpv_labs ♥️
@fpv_labs Lfg!
@fpv_labs LFG - we need to catchup soon!
@fpv_labs lfg!! 🚀
@fpv_labs LFGG!
@fpv_labs Really impressive work! 🔥 Have you tested FPV-SLAM on long egocentric videos with lots of motion blur? I’d love to try it with our camera + IMU data!
@fpv_labs sure can you pls email me at [email protected] - will connect with team who can help!
@fpv_labs Lfgg congrats on the launch
@fpv_labs Thanks buddy!
@fpv_labs Congrats, mono+IMU on head-worn footage is the hard track: fast head turns, motion blur, people moving all around you. Is the ranked run online, or offline with full-sequence optimization? Both are useful, but they suit different products.
@fpv_labs Congratulations!
@fpv_labs Thanks sir!
@fpv_labs congratss!!
@fpv_labs Thank youu!
@fpv_labs City-scale monocular-inertial SLAM is a serious challenge! Impressive benchmark result for FPV-SLAM. Robust localization and loop closure in complex environments are key to scalable autonomous navigation.
@fpv_labs Good job !
![This seemingly obvious prediction didn't take long to become reality. MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors Contributions: • The first real-time SLAM system using the two-view 3D reconstruction prior MASt3R [20] as a foundation. • Efficient techniques for pointmap matching, tracking and local fusion, graph construction and loop closure, and second-order global optimization. • A state-of-the-art dense SLAM system capable of handling generic, time-varying camera models. Abstract: We present a real-time monocular dense SLAM system, designed from the ground up using MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system remains robust on in-the-wild video sequences, making no assumptions on a fixed or parametric camera model beyond a unique camera center. Key features include: - Efficient methods for pointmap matching, camera tracking, and local fusion - Graph construction and loop closure - Second-order global optimization With known calibration, a simple modification achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.](https://image.24vids.com/tw-1868933842280604139/ext_tw_video_thumb/1868932917897871360/pu/img/WAmSelTOk2HJLm86.jpg)
