High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review

Li, Jianwei; Gao, Wei; Wu, Yihong; Liu, Yangdong; Shen, Yanfei

doi:10.1007/s41095-021-0250-8

Cited by 43 publications

(17 citation statements)

References 146 publications

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“…According to the data presented by Li et al [7] and the pose results obtained from experiments, table 4 can be made to show the error indicator (RMSE) of pose estimation obtained by different algorithms using the TUM dataset. From table 4, it can be seen that the optimized algorithm has higher accuracy than other algorithms in estimating the accuracy of estimated camera trajectories (ATE RMSE in mm).…”

Section: Experimental Results Of Pose Estimationmentioning

confidence: 99%

“…At present, many researchers have carried out detailed interpretation and evaluation of the overall framework of RGB-D visual SLAM and 3D reconstruction system. Many excellent open-source solutions also have been proposed [7]. In 2013, Labbé and Michaud proposed a representative RGB-D SLAM system RTAB-MAP [8] for the online loop detection problem in large environments.…”

Section: Introductionmentioning

confidence: 99%

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Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

Pan,

Hou,

et al. 2024

Meas. Sci. Technol.

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Simultaneous localization and mapping(SLAM) technology aims to solve the problem of how to determine the trajectory of robots by observing the environment and construct sparse spatial models. And the 3D reconstruction technology can further build dense spatial models based on the pose information. This paper proposes a dense 3D reconstruction algorithm based on improved accelerated-KAZE(AKAZE) features and a multi-layer feature detection network. A multi-layer feature detection neural network is constructed to replace the BoW model and optimize the loopback results, which improves the effect of pose estimation. The optimized algorithm improves the mapping effect and construct clear and robust indoor complex models. The pose estimation results show that the optimized algorithm improves the accuracy by 30% compared with ORB-SLAM2. Compared with the mapping effect of Kintinuous, ElasticFusion, ORB-SLAM2 and BundleFusion, the optimized models own the advantages of clearer details, richer information and no split layer.

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Section: Experimental Results Of Pose Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

Pan,

Hou,

et al. 2024

Meas. Sci. Technol.

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“…The popularization of RGB-D cameras, e.g., Microsoft Kinect, Asus Xtion Live, Intel RealSense, Google Tango, and Occiptial's Structure Sensor, makes it convenient to obtain 3D point clouds. However, compared to the point clouds acquired by LiDAR, those acquired by these cameras are low quality, because they are sensitive to the shininess, brightness, and transparency of surfaces [48]. Additionally, RGB-D cameras have a limited range, e.g., 0.8-2.5 m for Kinect v2.…”

Section: Resultsmentioning

confidence: 99%

A New Approach toward Corner Detection for Use in Point Cloud Registration

Wang

Zhang

et al. 2023

Remote Sensing

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For this study, a new point cloud alignment method is proposed for extracting corner points and aligning them at the geometric level. It can align point clouds that have low overlap and is more robust to outliers and noise. First, planes are extracted from the raw point cloud, and the corner points are defined as the intersection of three planes. Next, graphs are constructed for subsequent point cloud registration by treating corners as vertices and sharing planes as edges. The graph-matching algorithm is then applied to determine correspondence. Finally, point clouds are registered by aligning the corresponding corner points. The proposed method was investigated by utilizing pertinent metrics on datasets with differing overlap. The results demonstrate that the proposed method can align point clouds that have low overlap, yielding an RMSE of about 0.05 cm for datasets with 90% overlap and about 0.2 cm when there is only about 10% overlap. In this situation, the other methods failed to align point clouds. In terms of time consumption, the proposed method can process a point cloud comprising 104 points in 4 s when there is high overlap. When there is low overlap, it can also process a point cloud comprising 106 points in 10 s. The contributions of this study are the definition and extraction of corner points at the geometric level, followed by the use of these corner points to register point clouds. This approach can be directly used for low-precision applications and, in addition, for coarse registration in high-precision applications.

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“…When considering typical sensors, the Time-of-Flight (ToF) camera and Lidar (Light Detection and Ranging) are the most widely used modern sensors to obtain 3D information [86]. With a collection of data points maintaining depth images, the 3D point clouds could be generated to help the UAV understand the surroundings without sensor drift [87]. The typical approaches to maintain the visual-guided landing based on the ToF camera and Lidar are summarized in Table 2.…”

Section: Visual-guided Landingmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review

Cited by 43 publications

References 146 publications

Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

A New Approach toward Corner Detection for Use in Point Cloud Registration

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

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