Stereo Visual-Inertial Odometry With Multiple Kalman Filters Ensemble

Liu, Yong; Xiong, Rong; Wang, Yue; Huang, Hong; Xie, Xiaojia; Liu, Xiaofeng; Zhang, Gaoming

doi:10.1109/tie.2016.2573765

Cited by 64 publications

(26 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…E.g., the work by Hol et al (2007) presents an EKF based sensor fusion method for vision and IMU sensors including these features. Also, Liu et al (2016) have proposed recently a promising visual-inertial sensor fusion algorithm which could be easily integrated with our design, since position is estimated in a separate filter.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Augmented Reality in Forest Machine Cabin

Palonen¹,

Hyyti

Visala³

2017

IFAC-PapersOnLine

View full text Add to dashboard Cite

Augmented reality human machine interface is demonstrated in the cabin of a forest machine outdoors for the first time in real time. In this work, we propose a system setup and a real-time capable algorithm to augment the operator's visual field with measurements from the forest machine and its environment. In the demonstration, an instrumented forestry crane and a lidar are used to model the pose of the crane and its surroundings. In our approach, a camera and an inertial measurement unit are used to estimate the pose of the operator's head in difficult lighting conditions with the help of planar markers placed on the cabin structures. Using the estimate, a point cloud and a crane model are superimposed on the video feed to form an augmented reality view. Our system is tested to work outdoors using a forest machine research platform in real time with encouraging initial results.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Augmented Reality in Forest Machine Cabin

Palonen¹,

Hyyti

Visala³

2017

IFAC-PapersOnLine

View full text Add to dashboard Cite

show abstract

“…We also employ two basic visual odometry algorithms in our experiments. The first one is the opensource libviso2 [ 24 ] and the second one is a Stereo Visual Odometry (SVO) algorithm [ 25 ]. The SVO uses CenSurE detector [ 26 ] to detect the interesting points.…”

Section: Methodsmentioning

confidence: 99%

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

Zhao

Liu

Xie

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms.

show abstract

“…Test results indicate that the proposed fuzzy Kalman filter is more accurate than a conventional unscented Kalman filter. A new fusion method using a Kalman filter is developed in [36] for a vehicle. The fusion algorithm applies three Kalman filters operating at various fusion intervals to estimate attitude, orientation, and position.…”

Section: A Literature Reviewmentioning

confidence: 99%

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

et al. 2020

View full text Add to dashboard Cite

A new fusion strategy is introduced in this article to estimate state for multi-rate multisensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology. INDEX TERMS Multi-rate Kalman filter, estimation, sensor data fusion, missing measurements, OWA operator.

show abstract

Stereo Visual-Inertial Odometry With Multiple Kalman Filters Ensemble

Cited by 64 publications

References 24 publications

Augmented Reality in Forest Machine Cabin

Augmented Reality in Forest Machine Cabin

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

A New Fusion Estimation Method for Multi-Rate Multi-Sensor Systems With Missing Measurements

Contact Info

Product

Resources

About