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

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

cs.CVMar 2, 2026

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

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

Organizations: Department of Energy and Mechanical Engineering, Aalto University, 02150 Espoo, Finland

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.

Figures & tables

Panel of Figure 1 — Fig. 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.

Fig. 2 : Overview of the proposed event-based trajectory forecasting framework.

Fig. 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.

Method	Input		ADE (px) $\downarrow$		FDE (px) $\downarrow$
Method	RGB	Event	0.4s	0.8s	0.4s	0.8s
LSTM	✗	✗	171.9	353.3	53.04	96.44
Transformer	✗	✗	124.8	292.2	46.74	87.68
CNN + Transformer	✓	✗	124.0	280.9	45.92	83.86
CNN + Transformer	✗	✓	151.7	337.0	58.06	100.6
CNN + Transformer	✓	✓	121.3	281.5	45.23	85.78

TABLE I : Trajectory forecasting results on the FRED dataset. ADE and FDE are reported in pixels.

Fig. 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.

Trajectory Prediction

Trajectory prediction forecasts how people, vehicles, robots, and other dynamic systems will move from observed histories and context.

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Residual Kalman Dynamics for Event-Based UAV Forecasting

Per Nyblom, Hannes Ovrén, David Gustafsson

Swedish Defence Research Agency (FOI), Linköping, Sweden

Event-Based Vision Kalman Filtering Residual Learning UAV Trajectory Optimization Trajectory Prediction

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We study short- and mid-horizon UAV bounding-box forecasting on the FRED event-camera dataset. We use a constant-velocity Kalman filter over a full center-size box state as a strong physical baseline, and train a residual model to predict acceleration-like corrections from recent box history, filtered state features, and local event representations. This simple residual formulation consistently improves over the Kalman baseline, with event-conditioned models giving the strongest results among the evaluated methods. We further show that part of the residual target is predictable from anchor position and velocity alone, indicating that canonical FRED results can reflect both visual evidence and dataset-specific motion priors. To analyze this effect, we introduce decorrelated subsets as a diagnostic stress test, showing that event-conditioned residual models retain useful predictive signal even when measured position- and velocity-based shortcuts are weakened.

Abstract

Trajectory Prediction

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