Robust Navigation In GNSS Degraded Environment Using Graph Optimization

Watson, Ryan M.; Gross, Jason N.

doi:10.33012/2017.15164

Cited by 42 publications

(34 citation statements)

References 10 publications

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“…The MM approach was extended within the batch covariance estimation (BCE) framework [5], [22] to enable the estimation of the multi-modal covariance models during optimization. The BCE approach enables the estimation of the multi-modal covariance model through the utilization of variational clustering [23] on the current set of state estimation residuals.…”

Section: Robust Estimationmentioning

confidence: 99%

Robust Incremental State Estimation Through Covariance Adaptation

Watson

Gross

Taylor

et al. 2020

IEEE Robot. Autom. Lett.

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Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.

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Section: Robust Estimationmentioning

confidence: 99%

Robust Incremental State Estimation Through Covariance Adaptation

Watson

Gross

Taylor

et al. 2020

IEEE Robot. Autom. Lett.

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“…Soloviev has demonstrated that 2D line LIDAR scan matching can be paired with GPS for navigating in environments where GPS navigation alone can be difficult . More recently, these sensors have been combined in Simultaneous Localization and Mapping (SLAM) approaches, most commonly developed for indoor and GPS‐denied navigation. While SLAM techniques alone have the capability of allowing users to navigate by observing environmental changes, long term positioning often suffers from drift and biases.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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Because of the recent interest in unmanned air vehicle (UAV) commercialization, there is a great need for navigation algorithms that provide accurate and robust positioning in urban environments that are often Global Positioning Systems (GPS) challenged or denied. In this paper, we present a probabilistic graph-based navigation algorithm resilient to GPS errors. Fusing GPS pseudorange and Light Detection and Ranging (LiDAR) odometry measurements with 3D building maps, we apply a batch estimation approach to generate a robust trajectory estimate and maps of the surrounding environment. We then leverage the maps to locate potential sources of GPS multipath and mitigate the effects of degraded pseudorange measurements on the trajectory estimate. We experimentally validate our results with flight tests conducted in GPS-challenged and GPS-denied environments.

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“…However, the method relies heavily on the initial guess of the states to further calculate reliable residuals [33]. Similar work is done in [34]. Moreover, the M-estimator algorithm [35] is applied to further enhance robustness against GNSS outliers in [34].…”

Section: Introductionmentioning

confidence: 99%

“…Similar work is done in [34]. Moreover, the M-estimator algorithm [35] is applied to further enhance robustness against GNSS outliers in [34]. The principle of M-estimator in factor graph optimization is to embed the standard error function with an additional robust function, such as Cauchy [36] and Huber [36] functions.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, our recent work [41] shows that the excessive exclusion of DFP can distort the geometry of feature distribution, which can degrade the performance of VINS. Inspired by the work in [33,34,40] and our findings in [10,41], this paper proposes to estimate the sensor model of visual measurement (tracked feature) online based on the quality of feature tracking. Firstly, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurements by integrating two parts: 1) the geometry of feature distribution (GFD), 2) the quality of feature tracking.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

Bai

Wen

Hsu

2020

Preprint

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Visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature tracking process which is fatal to the feature-based VINS. The well-known method mitigates the effects of dynamic objects is to detect the vehicles using deep neural networks and remove the features belongs to the surrounding vehicle. However, excessive exclusion of features can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this paper proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of VINS. Firstly, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurements by integrating two parts: 1) the geometry of feature distribution (GFD), 2) the quality of feature tracking. Secondly, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the impacts of outlier measurements, such as the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance of excessive parameterization of M-estimator. Experiments are conducted in a typical urban area of Hong Kong with numerous dynamic objects, and the results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of VINS is obtained when compared with the conventional method.

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Robust Navigation In GNSS Degraded Environment Using Graph Optimization

Abstract: Robust navigation in urban environments has received a considerable amount of both academic and commercial interest

Cited by 42 publications

References 10 publications

Robust Incremental State Estimation Through Covariance Adaptation

Robust Incremental State Estimation Through Covariance Adaptation

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

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