Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for onboard state estimation of a high-speed UAV using a monocular RGB camera and an IMU. Our approach fuses data from Visual-Inertial Odometry (VIO), an onboard landmark-based camera measurement system, and an IMU to produce an accurate state estimate. Using onboard measurement data, we estimate and compensate for VIO drift through a novel mathematical drift model. State-of-the-art approaches often rely on more complex hardware (e.g., stereo cameras or rangefinders) and use uncorrected drifting VIO velocities, orientation, and angular rates, leading to errors during fast maneuvers. In contrast, our method corrects all VIO states (position, orientation, linear and angular velocity), resulting in accurate state estimation even during rapid and dynamic motion. Our approach was thoroughly validated through 1600 simulations and numerous real-world experiments. Furthermore, we applied the proposed method in the A2RL Drone Racing Challenge 2025, where our team advanced to the final four out of 210 teams and earned a medal.
Figures & tables
Figure 1: Long-exposure photo of drones carrying blue lights taken during a race flight at the A2RL Drone Racing Challenge 2025.
Figure 2: This figure presents the pipeline diagram of the estimation approach proposed in this paper, highlighted by the light blue dotted rectangle and integrated into our drone racing framework. The High-level mission planning block provides the desired position and heading references (rd,ηd) for the UAV to follow the race track. These references are passed to the Reference tracker , which generates a smooth and dynamically feasible reference χd for the Reference controller . The Reference controller computes the desired thrust and angular velocities (ωd,Td) for the embedded flight controller, which then sends actuator commands τcm to the UAV actuators . The VIO algorithm uses IMU data and onboard camera images to estimate the UAV position x , orientation R , linear velocity x˙ , and angular velocity ω . The VIO drift estimator estimates the drift in the VIO states using position x and orientation R measurements from the Landmark detector . The State estimator fuses data from the IMU , VIO drift estimator , and VIO algorithm to estimate the UAV position, orientation, and linear and angular velocities (x,x˙,R,ω) .
Figure 3: The depiction of coordinate frames consisting of the world frame W , UAV body frame Bw expressed in the W frame, VIO frame V , and UAV body frame Bv expressed in the V frame. The relative position and orientation between the W frame and the V frame are represented by rinitw . The UAV position and orientation in W frame are denoted as rgtw . The UAV position and orientation estimated by the VIO algorithm are provided in the V frame and denoted as rviov . The vector rviow represents the VIO -estimated UAV position and orientation expressed in the W frame. The VIO drift is expressed as rdriftw .
σpld2 [ m ]
0.001
0.01
0.1
1.0
0.001
0.01
0.1
1.0
σψld2 [ rad ]
0.001
0.01
0.1
1.0
0.001
0.01
0.1
1.0
τd [ ms ]
State
Our approach
VIO
0
p [ m ]
0.35
0.40
0.45
1.11
6.39
7.55
8.68
8.44
Θ [ rad ]
0.04
0.04
0.09
0.57
0.23
0.23
0.26
0.29
v [ ms−1 ]
0.96
1.00
1.09
1.40
1.27
1.30
1.49
1.57
ω [ rads−1 ]
0.24
0.23
0.24
0.24
1.13
1.15
1.11
1.09
Table 1: RMSE results from the simulations.
Figure 4: Custom-built outdoor track used to verify our approach.
Figure 6: 3D plot of the estimated UAV position using our approach, with color indicating the UAV speed. The VIO position is shown in grey. Measurements from the Landmark detector are represented as circles, with green color indicating confidence, and the race gates are shown as black circles.
Figure 5: Top view of the outdoor track showing the RTK states, results of our approach, VIO states, and data from the Landmark detector (LM det.).
Method
State
RMSE
Mean
Std. Dev.
Max
Our approach
p [ m ]
0.543
0.477
0.260
1.490
Θ [ rad ]
0.092
0.077
0.050
0.229
v [ ms−1 ]
1.262
1.036
0.722
3.689
ω [ rads−1 ]
0.103
0.074
0.071
0.709
Foehn et al. [ 9 ]
p [ m ]
0.645
0.535
0.359
2.396
Θ [ rad ]
0.179
0.163
0.074
0.343
Table 2: RMSE, mean error, standard deviation of the error, and maximum error of estimated UAV states using our approach compared to state-of-the-art, VIO, and Landmark detector.
Figure 7: Estimated UAV states during the A2RL finals using our approach.
Autonomous drone racing (ADR) demands state estimation that is simultaneously computationally efficient and resilient to the perceptual degradation experienced during extreme velocity and maneuvers. Traditional frameworks typically rely on conventional visual-inertial pipelines with loosely-coupled gate-based Perspective-n-Points (PnP) corrections that suffer from a rigid requirement for four visible features and information loss in intermediate steps. Furthermore, the absence of GNSS and Motion Capture systems in uninstrumented, competitive racing environments makes the objective evaluation of such systems remarkably difficult. To address these limitations, we propose ADR-VINS, a robust, monocular visual-inertial state estimation framework based on an Error-State Kalman Filter (ESKF) tailored for autonomous drone racing. Our approach integrates direct pixel reprojection errors from gate corners features as innovation terms within the filter. By bypassing intermediate PnP solvers, ADR-VINS maintains valid state updates with as few as two visible corners and utilizes robust reweighting instead of RANSAC-based schemes to handle outliers, enhancing computational efficiency. Furthermore, we introduce ADR-FGO, an offline Factor-Graph Optimization framework to generate high-fidelity reference trajectories that facilitate post-flight performance evaluation and analysis on uninstrumented, GNSS-denied environments. The proposed system is validated using TII-RATM dataset, where ADR-VINS achieves an average RMS translation error of 0.143 m, while ADR-FGO yields 0.060 m as a smoothing-based reference. Finally, ADR-VINS was successfully deployed in the A2RL Drone Championship Season 2, maintaining stable and robust estimation despite noisy detections during high-agility flight at top speeds of 20.9 m/s. We further utilize ADR-FGO for post-flight evaluation in uninstrumented racing environments.
Maulana Bisyir Azhari, Donghun Han, Sung Jun Park +1
Unmanned Systems Research Group (USRG), School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea.
Agile multi-UAV flight requires accurate and low-latency onboard estimation of the kinematic states of neighboring UAVs for collision avoidance, motion coordination, etc. Most vision-based approaches rely on position-only measurements, inferring velocity and acceleration indirectly from displacement. We show that this introduces a fixed structural delay in the estimation of higher-order states, which limits the achievable agility. To address this, we propose to integrate tilt measurements, provided by a state-of-the-art visual detector, which inform about the thrust direction of co-planar multirotor UAVs. We benchmark four position-only and five pose-aware estimators, including a novel formulation of a linear thrust-constraining Kalman filter, on two real-world and one high-fidelity photorealistic simulated dataset over different levels of agility (3-21 m/s^2). In our setup, pose-aware estimation consistently reduces the average velocity and acceleration estimation errors by 40% and 57% across the three datasets with the proposed KF formulation outperforming the other estimators. Position-only filters exhibit a constant ~300 ms delay in acceleration step response independent of agility, whereas the tilt-constrained estimators operate near the physical response limit given by the camera frame-rate by observing the change in thrust direction before the displacement accumulates. In a closed-loop leader-follower simulated experiment with NMPC control, position-only estimation of the leader's state fails to facilitate stable hovering of the follower, while the proposed estimator enables tracking of lateral maneuvers exceeding 2g of acceleration.
Michal Pliska, Matouš Vrba, Ondřej Víta +3
Multi-robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
A precise state estimate is crucial for a tight feedback control that enables agile and near-obstacle flights of UAVs. The state-of-the-art methods fuse slow pose measurements with high-frequency inertial measurements to obtain a precise state estimate. However, the inertial measurements from the IMU onboard the UAV are degraded by vibrations from spinning propellers and the precision of the estimated state suffers. We propose a novel approach based on the preintegration of accelerations obtained from motor speeds. We show that the accelerations obtained in this manner can be used for state propagation on their own to achieve better precision without including the IMU. Further, we propose a factor composed of the preintegrated motor speeds that can be directly employed in factor graph optimization frameworks. We combine our factor with LiDAR measurements into the proposed Motor Angular Speed LiDAR Odometry (MAS-LO) algorithm for precise state estimation, which we open-source. Lastly, we evaluate the estimation precision against a state-of-the-art inertial algorithm LIO-SAM to show 28% improvement in position and 65% in velocity estimation accuracy, 14% lower measurement lag, and high robustness to wrong parameter values.
Matěj Petrlík, Filip Novák, Robert Pěnička +1
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 36, Prague 6, Czech Republic