Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry

Xue, Fei; Wang, Xin; Li, Shunkai; Wang, Qiuyuan; Wang, Junqiu; Zha, Hongbin

doi:10.1109/cvpr.2019.00877

Cited by 105 publications

(70 citation statements)

References 35 publications

(171 reference statements)

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“…Risqi et al [55] includes geometrical loss restrictions in order to increase consistency between multiple poses. In addition, Xue et al [56] implemented a memory module for storing global information and a refinement module for enhancing pose estimation. However, all of the ego-motion approaches referred to above are supervised methods and need ground truth labels of poses to train.…”

Section: B Data-driven Methodsmentioning

confidence: 99%

LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

et al. 2021

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Deep learning (DL) based localization and Simultaneous Localization and Mapping (SLAM) has recently gained considerable attention demonstrating remarkable results. Instead of constructing handcrafted algorithms through geometric theories, DL based solutions provide a data-driven solution to the problem. Taking advantage of large amounts of training data and computing capacity, these approaches are increasingly developing into a new field that offers accurate and robust localization systems. In this work, the problem of global localization for unmanned aerial vehicles (UAVs) is analyzed by proposing a sequential, end-to-end, and multimodal deep neural network based monocular visual-inertial localization framework. More specifically, the proposed neural network architecture is threefold ; a visual feature extractor convNet network, a small IMU integrator bi-directional long short-term memory (LSTM), and a global pose regressor bi-directional LSTM network for pose estimation. In addition, by fusing the traditional IMU filtering methods instead of LSTM with the convNet, a more time-efficient deep pose estimation framework is presented. It is worth pointing out that the focus in this study is to evaluate the precision and efficiency of visual-inertial (VI) based localization approaches concerning indoor scenarios. The proposed deep global localization is compared with the various state-of-the-art algorithms on indoor UAV datasets, simulation environments and real-world drone experiments in terms of accuracy and time-efficiency. In addition, the comparison of IMU-LSTM and IMU-Filter based pose estimators is also provided by a detailed analysis. Experimental results show that the proposed filter-based approach combined with a DL approach has promising performance in terms of accuracy and time efficiency in indoor localization of UAVs.

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Section: B Data-driven Methodsmentioning

confidence: 99%

LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

et al. 2021

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“…It outperformed the likes of VISO2 [297], ORBSLAM [298] in KTTI dataset [299]. DeepVO was further improved by [300]- [302] for better generalization and memory function to upgrade the accuracy of pose estimation. Unsupervised methods have also been of great interest with works such as [293], [303]- [305].…”

Section: A Odometrymentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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“…There is a situation that if there is a timing relationship between the two input pictures, the problem can be called Visual Odometry (VO). Many studies have been carried out in this area, such as a new dynamic and effective strategy [30] proposed by Xue et al in 2019 to protect the VO pipeline that accumulates information during exercise. In the same year, Risqi et al proposed to use knowledge distillation [30] for camera pose regression, and use the confidence score to choose whether to transfer the knowledge learned by the teacher.…”

Section: B Relative Pose Regressionmentioning

confidence: 99%

“…Many studies have been carried out in this area, such as a new dynamic and effective strategy [30] proposed by Xue et al in 2019 to protect the VO pipeline that accumulates information during exercise. In the same year, Risqi et al proposed to use knowledge distillation [30] for camera pose regression, and use the confidence score to choose whether to transfer the knowledge learned by the teacher. There are also some methods [31]- [33] carried out through self-supervision, which have also achieved good results .…”

Section: B Relative Pose Regressionmentioning

confidence: 99%

RnR: Retrieval and Reprojection Learning Model for Camera Localization

Yang

Shi

2021

IEEE Access

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Camera localization is an essential technique in many applications, such as robot navigation, mixed reality, and unmanned vehicle. We are committed to solving the problem of predicting the 6-DoF pose of cameras from a single color image in a given three-Dimensional (3D) environment. In this paper, we proposed a robust learning model for it. Basically, our proposed methodology consists of two steps: image retrieval and space reprojection. The former is in charge of simultaneous localization and mapping based on pre-captured reference images that rely on the correspondence between pixel points and scene coordinates; whereas the latter carries out camera calibration between the 2D image plane and the 3D scene. Given a two-Dimensional (2D) image, the initial localization is accomplished rapidly by matching a reference image using Siamese networks. More precise localization is achieved by camera calibration between the 2D image and the 3D scene using a fully convolutional network. The experimental results on the public dataset show that our model is more robust and expandable than the previous methods. At the end of this paper, we also apply the system to Unmanned Aerial Vehicle (UAV) localization and achieve good results.

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Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry

Cited by 105 publications

References 35 publications

LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

RnR: Retrieval and Reprojection Learning Model for Camera Localization

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