Comparing Representations in Tracking for Event Camera-based SLAM

Jiao, Jianhao; Huang, Huaiyang; Li, Liang; He, Zhijian; Zhu, Yilong; Liu, Ming

doi:10.1109/cvprw53098.2021.00151

Cited by 25 publications

(26 citation statements)

References 26 publications

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“…In recent years, event camera has gained more attention increasingly. It has been applied to many computer vision tasks, including object recognition [47,52], segmentation [14], corner detection [59,79], gesture recognition [23,33,78], optical flow estimation [22,54], depth estimation [32,37], Simultaneous Localization And Mapping (SLAM) [19,43], and autonomous driving [25,51]. While RGB cameras struggle due to motion blur, event camera, by design, is highly sensitive to lux variation in both extremely overexposed and underexposed scene [18].…”

Section: Related Workmentioning

confidence: 99%

Neuromophic High-Frequency 3d Dancing Pose Estimation in Dynamic Environment

Zhang

Chai

et al. 2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Neuromophic High-Frequency 3d Dancing Pose Estimation in Dynamic Environment

Zhang

Chai

et al. 2023

Preprint

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“…Although event cameras can outperform standard visual sensors in severe illumination and dynamic range conditions, they mainly generate unsynchronized information about the environment. This makes traditional vision algorithms unable to process the outputs of these sensors [ 41 ]. Additionally, using the spatio-temporal windows of events along with the data obtained from other sensors can provide rich pose estimation and tracking information.…”

Section: Vslam Setupmentioning

confidence: 99%

Visual SLAM: What Are the Current Trends and What to Expect?

Tourani

Bavle

Sánchez-López

et al. 2022

Sensors

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In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous Localization and Mapping (VSLAM) methods refer to the SLAM approaches that employ cameras for pose estimation and map reconstruction and are preferred over Light Detection And Ranging (LiDAR)-based methods due to their lighter weight, lower acquisition costs, and richer environment representation. Hence, several VSLAM approaches have evolved using different camera types (e.g., monocular or stereo), and have been tested on various datasets (e.g., Technische Universität München (TUM) RGB-D or European Robotics Challenge (EuRoC)) and in different conditions (i.e., indoors and outdoors), and employ multiple methodologies to have a better understanding of their surroundings. The mentioned variations have made this topic popular for researchers and have resulted in various methods. In this regard, the primary intent of this paper is to assimilate the wide range of works in VSLAM and present their recent advances, along with discussing the existing challenges and trends. This survey is worthwhile to give a big picture of the current focuses in robotics and VSLAM fields based on the concentrated resolutions and objectives of the state-of-the-art. This paper provides an in-depth literature survey of fifty impactful articles published in the VSLAMs domain. The mentioned manuscripts have been classified by different characteristics, including the novelty domain, objectives, employed algorithms, and semantic level. The paper also discusses the current trends and contemporary directions of VSLAM techniques that may help researchers investigate them.

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“… We propose a semi-joint event-inertial initialization method to estimate initialization parameters, including scale, gravity direction, initial velocities, accelerometer, and gyroscope biases, in two steps (event-only initialization and event-inertial initial optimization). We implement the event-based stereo VIO system, ESVIO, in C++ and evaluate it on four public datasets [ 25 , 26 , 27 , 28 ]. The results demonstrate that our system achieves good accuracy and robust performance when compared with the state-of-the-art, and, at the same time, with no compromise to real-time performance.…”

Section: Introductionmentioning

confidence: 99%

ESVIO: Event-Based Stereo Visual-Inertial Odometry

Liu

Shi

et al. 2023

Sensors

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The emerging event cameras are bio-inspired sensors that can output pixel-level brightness changes at extremely high rates, and event-based visual-inertial odometry (VIO) is widely studied and used in autonomous robots. In this paper, we propose an event-based stereo VIO system, namely ESVIO. Firstly, we present a novel direct event-based VIO method, which fuses events’ depth, Time-Surface images, and pre-integrated inertial measurement to estimate the camera motion and inertial measurement unit (IMU) biases in a sliding window non-linear optimization framework, effectively improving the state estimation accuracy and robustness. Secondly, we design an event-inertia semi-joint initialization method, through two steps of event-only initialization and event-inertia initial optimization, to rapidly and accurately solve the initialization parameters of the VIO system, thereby further improving the state estimation accuracy. Based on these two methods, we implement the ESVIO system and evaluate the effectiveness and robustness of ESVIO on various public datasets. The experimental results show that ESVIO achieves good performance in both accuracy and robustness when compared with other state-of-the-art event-based VIO and stereo visual odometry (VO) systems, and, at the same time, with no compromise to real-time performance.

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Comparing Representations in Tracking for Event Camera-based SLAM

Cited by 25 publications

References 26 publications

Neuromophic High-Frequency 3d Dancing Pose Estimation in Dynamic Environment

Neuromophic High-Frequency 3d Dancing Pose Estimation in Dynamic Environment

Visual SLAM: What Are the Current Trends and What to Expect?

ESVIO: Event-Based Stereo Visual-Inertial Odometry

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