D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

Yang, Nan; Stumberg, Lukas von; Wang, Rui; Cremers, Daniel

doi:10.1109/cvpr42600.2020.00136

Cited by 362 publications

(262 citation statements)

References 57 publications

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“…The aforementioned camera pose estimation is also related to visual odometry (VO) and visual-inertial odometry (VIO) [133], [134], which aim to calculate sequential camera poses of an agent based on the camera and Inertial Measurement Unit (IMU) sensors. VO and VIO are always used ad the front-end in a SLAM system, where the back-end refers to the nonlinear optimization of the pose graph, aiming to obtain globally consistent and drift-free pose estimation results.…”

Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

466

168

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In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.

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Section: ) Text Spottingmentioning

confidence: 99%

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Zhang

Tao

2021

IEEE Internet Things J.

466

168

View full text Add to dashboard Cite

show abstract

“…Three key advantages to using deep learning methods in mobile robot systems are [19]: data without much mathematical modeling for different conditions such as abnormal lighting, blur motion, or cases where the camera needs calibration by hand, which is hard to do [287]. These models are able to match in some ways human-level reasoning and apply it in methods such as SLAM to create semantic maps [288], [289], or taking visual odometry to the next level [290], [291].…”

Section: Integration Of Deep Learning In Mobile Robotsmentioning

confidence: 99%

“…Hybrid methods combining classical models and deep learning approaches have seen a higher accuracy in terms of performance. Some hybrid algorithms like D3VO [291]) even outperform VIO algorithms such as DeepVIO [309], VIOLearner [307] as well as classical VO systems such as DSO [310], ORB SLAM [298] in KTTI benchmarks.…”

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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“…A common assumption used by current works is photometric consistency, that is, the photometric error of corresponding pixel of the same object in different frames is zero. The photometric consistency assumption is often not satisfied because of brightness change and non-Lambertian surface [ 19 ]. To overcome these issues, GeoNet [ 11 ] added structural similarity (SSIM) [ 20 ] to loss to mitigate the effects of brightness change.…”

Section: Introductionmentioning

confidence: 99%

“…SSIM captures more local information than SAD, but it does not capture global information. D3VO [ 19 ] predicted the global transformation parameters

through a network, and adjusts the image I to

. However, D3VO only pays attention to the global brightness change, which is often hard to be satisfied in the real scene.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning of Depth and Camera Pose with Feature Map Warping

Guo

Chen

Zhou

et al. 2021

Sensors

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Estimating the depth of image and egomotion of agent are important for autonomous and robot in understanding the surrounding environment and avoiding collision. Most existing unsupervised methods estimate depth and camera egomotion by minimizing photometric error between adjacent frames. However, the photometric consistency sometimes does not meet the real situation, such as brightness change, moving objects and occlusion. To reduce the influence of brightness change, we propose a feature pyramid matching loss (FPML) which captures the trainable feature error between a current and the adjacent frames and therefore it is more robust than photometric error. In addition, we propose the occlusion-aware mask (OAM) network which can indicate occlusion according to change of masks to improve estimation accuracy of depth and camera pose. The experimental results verify that the proposed unsupervised approach is highly competitive against the state-of-the-art methods, both qualitatively and quantitatively. Specifically, our method reduces absolute relative error (Abs Rel) by 0.017–0.088.

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D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry

Cited by 362 publications

References 57 publications

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things

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

Unsupervised Learning of Depth and Camera Pose with Feature Map Warping

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