Relative State Estimation using Event-Based Propeller Sensing
- URL: http://arxiv.org/abs/2604.18289v1
- Date: Mon, 20 Apr 2026 13:59:44 GMT
- Title: Relative State Estimation using Event-Based Propeller Sensing
- Abstract summary: This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing.<n>Our approach estimates the propeller frequency under 3% error on a test dataset of five real-world outdoor flight sequences.
- Score: 3.0109926137673817
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Autonomous swarms of multi-Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame-based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond-level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event-based propeller sensing. The propellers in the event stream are tracked by detection to extract the region-of-interests. The event streams in these regions are processed in temporal chunks to estimate per-propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera-derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body-frame tilt-axis. The existing event-based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real-world outdoor flight sequences, providing a method for decentralized relative localization for multi-robot systems using event camera.
Related papers
- Event-Based Motion Estimation via Oriented Distance Fields [55.23416502666468]
Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion.<n>We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors.<n>On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods.
arXiv Detail & Related papers (2026-08-25T08:27:42Z) - Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
arXiv Detail & Related papers (2026-04-27T04:36:41Z) - EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
arXiv Detail & Related papers (2025-03-14T13:15:54Z) - MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
arXiv Detail & Related papers (2024-12-02T09:13:29Z) - ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras [41.992980062962495]
Event-based visual odometry aims at solving tracking and mapping subproblems (typically in parallel)<n>We build an event-based stereo visual-inertial odometry system on top of a direct pipeline.<n>The resulting system scales well with modern high-resolution event cameras.
arXiv Detail & Related papers (2024-10-12T05:35:27Z) - Event-Based Visual Odometry on Non-Holonomic Ground Vehicles [20.847519645153337]
Event-based visual odometry is shown to be reliable and robust in challenging illumination scenarios.
Our algorithm achieves accurate estimates of the vehicle's rotational velocity and thus results that are comparable to the delta rotations obtained by frame-based sensors under normal conditions.
arXiv Detail & Related papers (2024-01-17T16:52:20Z) - Tight Fusion of Events and Inertial Measurements for Direct Velocity
Estimation [20.002238735553792]
We propose a novel solution to tight visual-inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera.
We demonstrate how velocity estimates in highly dynamic situations can be obtained over short time intervals.
Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation.
arXiv Detail & Related papers (2024-01-17T15:56:57Z) - A 5-Point Minimal Solver for Event Camera Relative Motion Estimation [47.45081895021988]
We introduce a novel minimal 5-point solver that estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines.
Our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%.
arXiv Detail & Related papers (2023-09-29T08:30:18Z) - Event-based Stereo Visual Odometry with Native Temporal Resolution via
Continuous-time Gaussian Process Regression [3.4447129363520332]
Event-based cameras capture individual visual changes in a scene at unique times.
It is often addressed in visual odometry pipelines by approximating temporally close measurements as occurring at one common time.
This paper presents a complete stereo VO pipeline that estimates directly with individual event-measurement times without requiring any grouping or approximation in the estimation state.
arXiv Detail & Related papers (2023-06-01T22:57:32Z) - Asynchronous Optimisation for Event-based Visual Odometry [53.59879499700895]
Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.
We focus on event-based visual odometry (VO)
We propose an asynchronous structure-from-motion optimisation back-end.
arXiv Detail & Related papers (2022-03-02T11:28:47Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.