FPV Labs
@fpv_labs • 1,085 subscribers
Research and product lab working on human representations for robot learning
Videos
We are publishing our first deep dive on what we believe is one of the most challenging layers in egocentric data - SLAM and VIO in the context of long-horizon state tracking. We break down how SLAM and VIO fail in egocentric settings - visual features vanish at close range, depth sensors saturate, fast head motion blurs frames, and these failures don't always occur in isolation. They hit at the exact same moment, leading to compounding errors and making the downstream data unusable. We believe the foundation for high-quality egocentric data demands sub-centimeter precision over long episodes ranging from a few minutes to up to an hour.
FPV Labs41,955 görüntüleme • 3 ay önce
Introducing Project Stera by FPV Labs, an open data infra for embodied AI research. Project Stera includes Stera-10M, with 10M+ frames of long-horizon data with persistent state tracking, and an open-source pipeline that converts raw data into training-ready formats.
FPV Labs15,258 görüntüleme • 2 ay önce
We are publishing our second deep dive today as a follow-up post on SLAM and VIO in egocentric tracking. We go deep into the sensor tradeoffs b/w global shutter and rolling shutter and their implications on SLAM / VIO - specifically how the way the camera reads each frame can introduce significant tracking errors before our SLAM pipeline even starts processing. We break down why global shutter is the obvious fix but the wrong default, the physics of why rolling shutter dominates every consumer device, and where the fundamental limits lie.
FPV Labs16,732 görüntüleme • 3 ay önce
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