IMU aided RGB-D SLAM

Qayyum, Usman; Ahsan, Q.; Mahmood, Zahid

doi:10.1109/ibcast.2017.7868075

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Although this EKF variant improves the robustness and efficiency of SLAM in particular cases, it still fails if there are no features in the scene. Another variation of EKF-SLAM is proposed in [100] IMU measurements are used in the prediction phase and RGB-D images are used in the update phase. To achieve global consistency, pose graph optimization is performed.…”

Section: Resilience To Feature Detection/association Failurementioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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Section: Resilience To Feature Detection/association Failurementioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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“…For example, a pose graph optimization framework is proposed to realize real-time and highly accurate SLAM [21,22]. In addition, the conventional bundle adjustment framework is extended to incorporate the RGB-D data with inertial measurement unit to optimize mapping and pose estimation in an integrated manner [23][24][25]. The inductively coupled plasma (ICP) method is used to estimate the motion between continuous frames containing dense point cloud, and is effectively applied to RGB-D SLAM [26,27].…”

Section: Related Workmentioning

confidence: 99%

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

Kang

Fan

et al. 2019

Applied Sciences

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In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

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“…Aiming at the problem of less texture and ambiguous environment, Qayyum et al. [33] propose an RGB-D SLAM method combining the inertial navigation information in 2017, which can effectively reduce the error of pose estimation.…”

Section: Related Workmentioning

confidence: 99%

A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

Li¹,

Zhang²,

Huang³

2019

Sensors

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In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.

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IMU aided RGB-D SLAM

Cited by 9 publications

References 10 publications

Feature-based visual simultaneous localization and mapping: a survey

Feature-based visual simultaneous localization and mapping: a survey

Real-Time RGB-D Simultaneous Localization and Mapping Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation

A Novel RGB-D SLAM Algorithm Based on Cloud Robotics

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