IMU and Multiple RGB-D Camera Fusion for Assisting Indoor Stop-and-Go 3D Terrestrial Laser Scanning

Chow, Jacky C. K.; Lichti, Derek D.; Hol, Jeroen D.; Bellusci, Giovanni; Luinge, Hendrik J.

doi:10.3390/robotics3030247

Cited by 41 publications

(32 citation statements)

References 68 publications

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“…Integrating IMU into scanning systems is closely bounded up with sensor fusion topics. A conventional strategy is using Kalman filter related algorithms to fuse IMU and depth based tracking together [6]. Alternatively, pushing IMU output as an energy term into the tracking optimization function receives more attention recently, as it forms a more general and flexible framework.…”

Section: Introductionmentioning

confidence: 99%

Robust 3D Surface Reconstruction in Real-Time with Localization Sensor

Chun-lin

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYWe present a system to improve the robustness of realtime 3D surface reconstruction by utilizing non-inertial localization sensor. Benefiting from such sensor, our easy-to-build system can effectively avoid tracking drift and lost comparing with conventional dense tracking and mapping systems. To best fusing the sensor, we first adopt a handeye calibration and performance analysis for our setup and then propose a novel optimization framework based on adaptive criterion function to improve the robustness as well as accuracy. We apply our system to several challenging reconstruction tasks, which show significant improvement in scanning robustness and reconstruction quality. key words: dense tracking and mapping, surface reconstruction, sensor fusion, RGB-D SLAM

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Section: Introductionmentioning

confidence: 99%

Robust 3D Surface Reconstruction in Real-Time with Localization Sensor

Chun-lin

et al. 2018

IEICE Trans. Inf. & Syst.

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“…O método proposto mostrou melhoras significativas na trajetória do sensor e a potencialidade do emprego de pesos nas observações, entretanto, o algoritmo implementado não é robusto para ambientes internos com regiões pouco texturizadas. Chow et al (2014) desenvolveram um sistema híbrido de mapeamento 3D de ambientes internos. Os autores integraram sensores RGB-D em um sistema laser scanning terrestre móvel.…”

Section: Introductionunclassified

Um Método Adaptativo Para Registro De Dados RGB-D

et al. 2016

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Resumo:Neste trabalho é apresentado um método adaptativo para registro e mapeamento de ambientes internos com regiões de pouca textura usando dados derivados de câmeras RGB-D. Basicamente, quatro pontos principais envolvidos no desenvolvimento do método são aqui discutidos: a calibração dos sensores integrados no dispositivo Kinect; o registro dos pares de nuvens de pontos 3D adquiridas sucessivamente pelo sensor de imageamento; a inclusão de pesos no algoritmo ICP baseado em abordagem ponto-a-ponto; e o refinamento dos parâmetros de transformação empregando uma análise de consistência global. A calibração dos sensores é feita usando o método de Zhang. O modelo de corpo rígido 3D é empregado para calcular os parâmetros de transformação. Um método adaptativo é proposto para inclusão de pesos baseados na precisão teórica das medidas de profundidade. Um modelo linear capaz de refinar simultaneamente o conjunto de parâmetros de transformação é proposto para a análise da consistência global. Experimentos foram conduzidos para avaliar a eficiência do método proposto e os resultados obtidos mostraram sua potencialidade.Palavras chaves: câmeras RGB-D; calibração de câmeras; mapeamento de ambientes internos; registro de pares de nuvens de pontos; análise de consistência global, ICP. Abstract:In this paper an adaptive method for indoor mapping and registration of environment with texture less regions using RGB-D data is presented. Basically, four main points involved in the method are here discussed: the calibration of the sensors integrated in the Kinect device; the registration of 3D point clouds pairs acquired successively by the imaging sensor; the inclusion of weights in the point-to-point ICP algorithm; and a global adjustment for the refinement of transformation parameters. The sensors calibration is realized using the Zhang method. The 3D rigid body model is employed to calculate the transformation parameters. The weights are defined based on the theoretical accuracy of depth measurements. A linear model is proposed for global adjustment. Experiments were conducted and the results showed the efficiency and potentially of the proposed method.

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“…The correction enhanced the data quality derived from the Kinect V2 ToF sensor and thus develops its potential of being better adopted in applications such as close-range archaeology modeling [9] and indoor SLAM [10], where high depth accuracy is essential. Once the individual geometry and depth calibration of the Kinect V2s were completed, the exterior orientation parameters (EoPs) of the Kinect V2s in the RGB-D sensor array were calculated in a coarse-to-fine approach.…”

Section: Depth Calibrationmentioning

confidence: 99%

“…Most of the consumer RGB-D sensors (namely the Kinect and Intel RealSense) are originally designed for human-machine interaction in applications such as gaming, where metric measurements are not the focus. Thus, consumer RGB-D sensors need to be calibrated individually to refine the manufacturer's batch calibration parameters [10]. RGB-D sensors are a new kind of sensor.…”

Section: Introductionmentioning

confidence: 99%

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

et al. 2018

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Abstract:Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.

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IMU and Multiple RGB-D Camera Fusion for Assisting Indoor Stop-and-Go 3D Terrestrial Laser Scanning

Cited by 41 publications

References 68 publications

Robust 3D Surface Reconstruction in Real-Time with Localization Sensor

Robust 3D Surface Reconstruction in Real-Time with Localization Sensor

Um Método Adaptativo Para Registro De Dados RGB-D

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

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