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Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering

Hari Prasanth S. M., Pejman Habibiroudkenar, Eerik Alamikkotervo, Dimitrios Bouzoulas, Risto Ojala

TL;DR

This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues and demonstrates robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.

Abstract

Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.

Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering

TL;DR

This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues and demonstrates robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.

Abstract

Event cameras provide high-temporal-resolution visual sensing that is well suited for observing fast-moving aerial objects; however, their use for drone trajectory prediction remains limited. This work introduces an event-only drone forecasting method that exploits propeller-induced motion cues. Propeller rotational speed are extracted directly from raw event data and fused within an RPM-aware Kalman filtering framework. Evaluations on the FRED dataset show that the proposed method outperforms learning-based approaches and vanilla kalman filter in terms of average distance error and final distance error at 0.4s and 0.8s forecasting horizons. The results demonstrate robust and accurate short- and medium-horizon trajectory forecasting without reliance on RGB imagery or training data.
Paper Structure (16 sections, 4 equations, 4 figures, 1 table)

This paper contains 16 sections, 4 equations, 4 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Drone Trajectory Forecasting with On-board Sensors
  4. Remote Drone Trajectory Forecasting
  5. Event Cameras and Drone Trajectory Forecasting
  6. METHODOLOGY
  7. RPM Estimation
  8. Trajectory Forecasting Model
  9. Motion model
  10. Measurement model
  11. Forecasting
  12. FRED Dataset
  13. Evaluation
  14. Results
  15. Discussion
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Overview of the proposed trajectory forecasting up to 0.4 s into the future. Blue denotes the current drone position and past trajectory. Future ground-truth positions are shown in green, and predictions in orange.
  • Figure 2: Overview of the proposed event-based trajectory forecasting framework.
  • Figure 3: Qualitative trajectory forecasting results under challenging conditions. The current ground-truth position and past trajectory are shown in blue; future ground-truth positions are shown in green; predicted future positions are shown in orange. Results are illustrated across highly dynamic motion, night-time, indoor, and rain scenarios.
  • Figure 4: Distribution of forecasting errors at 0.4 s and 0.8 s horizons over 54 test cases. Top: ADE, Bottom: FDE. The box represents the interquartile range (25th–75th percentile), whiskers denote the 5th–95th percentile, and the orange colored line indicates the median.