Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

Marković, Lovro; Kovac, Marin; Milijas, Robert; Car, Marko; Bogdan, Stjepan

doi:10.48550/arxiv.2109.04908

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…Using single image 6-DoF PnP pose estimates as measurements, we follow the standard approach [61], [64] to correct p k , v k , q k , C k , where the posterior estimates of state variables are obtained by adding the prior estimates and the gain-weighted residual (i.e., the difference between the prior estimate and the current measurement). In our experimental results, this setup is referred to as the baseline EKF.…”

Section: B Ekf Pose Refinement With Imu -Baseline Systemmentioning

confidence: 99%

Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems

Hausler

Garg

et al. 2022

IEEE Robot. Autom. Lett.

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Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences. We contribute an algorithm which refines an initial set of candidate object detections and produces a refined subset of highly accurate detections using a prior map. We begin by using visual place recognition (VPR) to retrieve a reference map image for a given query image, then use a binary classification neural network that compares the query and mapping image regions to validate the query detection. Once our classification network is trained, on approximately 1000 query-map image pairs, it is able to improve the performance of vehicle detection when combined with an existing off-the-shelf vehicle detector. We demonstrate our approach using standard datasets across two cities (Oxford and Zurich) under different settings of train-test separation of map-query traverse pairs. We further emphasize the performance gains of our approach against alternative design choices and show that VPR suffices for the task, eliminating the need for precise ground truth localization.

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Section: B Ekf Pose Refinement With Imu -Baseline Systemmentioning

confidence: 99%

Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems

Hausler

Garg

et al. 2022

IEEE Robot. Autom. Lett.

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“…Due to the need to reduce the costs of an individual UAV and its small size, the used sensors are characterized by significant noise and other measurement errors, such as bias or incorrect scaling. Some of these errors are minimized through special calibration procedures [ 1 , 2 ]. However, scaling and bias errors are not constant over time, so regardless of calibration, it is necessary to monitor and compensate for these errors during flight.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the EKF filter can only be based on the fusion of data from the UAV’s own sensors. Depending on flight conditions and the number and type of sensors, the flight can continue or must be interrupted due to possible calculation errors of the EKF filter [ 2 , 19 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs

Szczepaniak,

Szlachetko,

Lower

2024

Sensors

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This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone’s attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system’s behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.

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Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

Cited by 2 publications

References 19 publications

Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems

Improving Worst Case Visual Localization Coverage via Place-Specific Sub-Selection in Multi-Camera Systems

The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs

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