The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

Schubert, David; Goll, Thore; Demmel, Nikolaus; Usenko, Vladyslav; Stückler, Jörg; Cremers, Daniel

doi:10.1109/iros.2018.8593419

Cited by 282 publications

(178 citation statements)

References 23 publications

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“…Back-end optimization techniques are usually implemented on g2o [99], ceres-solver [100], and gtsam [101]. Many excellent datasets can be used to study visual-inertial methods, such as EuRoC [102], Canoe [103], Zurich urban MAV [104], TUM VI Benchmark [105], and PennCOSYVIO [106]. Details of the study surveys are provided in Appendix B.…”

Section: Optimization-based Methodsmentioning

confidence: 99%

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Chen

Zhu

et al. 2018

Robotics

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Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide applications in the field of localization and mapping, including in mobile robotics, self-driving cars, unmanned aerial vehicles, and autonomous underwater vehicles. This study provides a comprehensive survey on VI-SLAM. Following a short introduction, this study is the first to review VI-SLAM techniques from filtering-based and optimization-based perspectives. It summarizes state-of-the-art studies over the last 10 years based on the back-end approach, camera type, and sensor fusion type. Key VI-SLAM technologies are also introduced such as feature extraction and tracking, core theory, and loop closure. The performance of representative VI-SLAM methods and famous VI-SLAM datasets are also surveyed. Finally, this study contributes to the comparison of filtering-based and optimization-based methods through experiments. A comparative study of VI-SLAM methods helps understand the differences in their operating principles. Optimization-based methods achieve excellent localization accuracy and lower memory utilization, while filtering-based methods have advantages in terms of computing resources. Furthermore, this study proposes future development trends and research directions for VI-SLAM. It provides a detailed survey of VI-SLAM techniques and can serve as a brief guide to newcomers in the field of SLAM and experienced researchers looking for possible directions for future work.

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Section: Optimization-based Methodsmentioning

confidence: 99%

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Chen

Zhu

et al. 2018

Robotics

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“…2) The TUM-VI benchmark [61], which contains 28 stereoinertial sequences of indoor and outdoor environments. Only the 6 sequences (i.e.…”

Section: A Benchmarksmentioning

confidence: 99%

Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency

Zhao

Vela

2020

IEEE Trans. Robot.

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Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency). Feature-based systems exhibit good performance, yet have higher latency due to explicit data association; direct & semidirect systems have lower latency, but are inapplicable in some target scenarios or exhibit lower accuracy than feature-based ones. This paper aims to fill the performanceefficiency gap with an enhancement applied to feature-based VSLAM. We present good feature matching, an active map-toframe feature matching method. Feature matching effort is tied to submatrix selection, which has combinatorial time complexity and requires choosing a scoring metric. Via simulation, the Max-logDet matrix revealing metric is shown to perform best. For real-time applicability, the combination of deterministic selection and randomized acceleration is studied. The proposed algorithm is integrated into monocular & stereo feature-based VSLAM systems. Extensive evaluations on multiple benchmarks and compute hardware quantify the latency reduction and the accuracy & robustness preservation.Index Terms-visual odometry (VO), visual simultaneous localization and mapping (VSLAM), feature selection, active matching

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“…We utilized gyroscope readings to generate realistic blur fields and blurred images. Specifically, we use the sequences room1 -room6 from an existing visual-inertial dataset [19]. These sequences consist of various types of camera motion, which results to a diverse set of blur fields with varying lev- els of spatially-variant motion blur.…”

Section: Data Generationmentioning

confidence: 99%

Gyroscope-Aided Motion Deblurring with Deep Networks

Mustaniemi

Kannala

Särkkä

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We propose a deblurring method that incorporates gyroscope measurements into a convolutional neural network (CNN). With the help of such measurements, it can handle extremely strong and spatially-variant motion blur. At the same time, the image data is used to overcome the limitations of gyro-based blur estimation. To train our network, we also introduce a novel way of generating realistic training data using the gyroscope. The evaluation shows a clear improvement in visual quality over the state-of-the-art while achieving real-time performance. Furthermore, the method is shown to improve the performance of existing feature detectors and descriptors against the motion blur.

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The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

Cited by 282 publications

References 23 publications

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

A Review of Visual-Inertial Simultaneous Localization and Mapping from Filtering-Based and Optimization-Based Perspectives

Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency

Gyroscope-Aided Motion Deblurring with Deep Networks

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