Multi-sensor robust relative estimation framework for GPS-denied multirotor aircraft

Koch, Daniel P.; McLain, Timothy W.; Brink, Kevin

doi:10.1109/icuas.2016.7502619

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Covariance has been a widely used measurement for error estimation [16][17] [18]. Many of these methods correlate pose uncertainty with the covariance of data matching [19] [20], which could be an ill-posed problem in many situations.…”

Section: Introductionmentioning

confidence: 99%

Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

Zhao

2020

Preprint

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Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which the error model plays a paramount role. However, exteroceptive sensor-based odometries (ESOs), such as LiDAR/visual odometry, often deliver results with scene-related error, which is difficult to model accurately. To address this problem, this research designs a scene-aware error model for ESO, based on which a multimodal localization fusion framework is developed. In addition, an end-to-end learning method is proposed to train this error model using sparse global poses such as GPS/IMU results. The proposed method is realized for error modeling of LiDAR/visual odometry, and the results are fused with dead reckoning to examine the performance of vehicle localization. Experiments are conducted using both simulation and realworld data of experienced and unexperienced environments, and the experimental results demonstrate that with the learned scene-aware error models, vehicle localization accuracy can be largely improved and shows adaptiveness in unexperienced scenes.

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Section: Introductionmentioning

confidence: 99%

Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

Zhao

2020

Preprint

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“…The transformation that aligns corresponding measurements in sequential frames indicates how the camera orientation and position changed between these frames (delta pose), which can provide a vehicle odometry measurements suitable for propagation of position estimates. Additionally, recent navigation filter developments are using delta pose as a direct measurement update [3,4]. Finally, delta-pose RGBD odometry may also be appropriate as a velocity estimate, assuming that the time step between images is well known.…”

mentioning

confidence: 99%

“…As such, navigational systems that seek to best leverage RGBD odometry require uncertainty estimates for these parameters. Absence of this uncertainty estimate implies that hand-specified or ad hoc estimates must be used [3,4]. In these instances, the designers experimentally tune a single fixed value of covariance that treats the expected quality of the delta-pose measurement equally, regardless of the scene observed.…”

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confidence: 99%

Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

Anderson

Brink

Willis

2019

Journal of Guidance, Control, and Dynamics

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Demand is growing for unmanned air vehicles (UAVs) with greater autonomy, including the ability to navigate without GPS information, such as indoors. In this work, a novel visual odometry algorithm is developed and flight tested. It uses sequential pairs of red, green, blue, depth (RGBD) camera images to estimate the UAV's change in position (delta pose), which can be used to aid a navigation filter. Unlike existing related techniques, it uses a novel perturbation approach to estimate the uncertainty of the odometry measurement dynamically in real time, a technique that is applicable to a wide range of sensor preprocessing tasks aimed at generating navigation-relevant measurements. Real-time estimates of the delta pose and its covariance allow these estimates to be efficiently fused with other sensors in a navigation filter. Indoor flight testing was performed with motion capture, which demonstrated that the odometry and covariance estimates are accurate when appropriately scaled. Flights also demonstrated the algorithm used in a navigation filter to improve a velocity estimate, which represents a significant improvement over the state of the art for RGBD odometry.

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Relative navigation of autonomous GPS-degraded micro air vehicles

et al. 2020

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Many current approaches for navigation of micro air vehicles (MAVs) in GPS-degraded environments use a globally-referenced state for estimation and control, even though this state is not observable when GPS is unavailable. By working with respect to a local reference frame, the relative navigation (RN) framework presented in this paper ensures that the state maintains observability and that the uncertainty remains bounded, consistent, and normally-distributed. RN further insulates flight-critical estimation and control processes from the large global updates common in GPSdegraded MAV flight. This paper provides a thorough description of the details needed to successfully implement the RN framework on a MAV. The practicality of RN is demonstrated in several long flight tests in unknown, GPS-denied and GPS-degraded environments. The relative front end is shown to produce low-drift estimates and smooth, stable control while leveraging off-the-shelf algorithms. The system runs in real time with onboard processing, fuses a variety of vision sensors, works indoors and outdoors, and does not require special tuning for particular sensors or environments. RN is also shown to produce globally-consistent, metric, and localized maps by incorporating loop closures and intermittent GPS measurements. This map is used to demonstrate autonomous completion of mission objectives. By subtly restructuring the estimation framework, RN promotes a paradigm shift that avoids many issues inherent in GPS-degraded navigation.

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Multi-sensor robust relative estimation framework for GPS-denied multirotor aircraft

Cited by 8 publications

References 15 publications

Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

Real-Time Visual Odometry Covariance Estimation for Unmanned Air Vehicle Navigation

Relative navigation of autonomous GPS-degraded micro air vehicles

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