Kinect v2 for mobile robot navigation: Evaluation and modeling

Fankhauser, Péter; Bloesch, Michael; Rodríguez, Diego; Kaestner, Ralf; Hutter, Marco; Siegwart, Roland

doi:10.1109/icar.2015.7251485

Cited by 261 publications

(164 citation statements)

References 11 publications

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“…In uncontrolled environments, the Kinect V2 depth measurements are influenced by temperature, material, ambient light, and multipath effect [36]. The high error distribution at the edges of objects with complex shape whose local curvature changes severely is caused by a shadow effect [62] in complex edges. This is the outcome of the different optical axis of the infrared emitter of the infrared camera.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…Since its launch, the Kinect has been proficiently used for a number of robotic applications, both for navigation and mapping tasks (see for instance Suarez and Murphy, 2012, Omara and Sahari, 2015, Fankhauser et al, 2015. Quite often, the proposed solutions are based on RGB-D SLAM; they can take advantage of pure depth information (Izadi et al, 2011) or depth-coloured data (Endres et al, 2012).…”

Section: Outdoor Navigation With the Kinectmentioning

confidence: 99%

“…To our knowledge, the use of Microsoft Kinect for outdoor navigation has been considered by few authors. Quayyum and Kim (2013) discussed an inertial-SLAM fused solution for outdoor navigation, while Fankhauser et al (2015) evaluated the impact of the light incidence angle for depth measurements.…”

Section: Outdoor Navigation With the Kinectmentioning

confidence: 99%

Integration of Kinect and Low-Cost GNSS for Outdoor Navigation

Pagliaria¹,

Pinto²,

Reguzzoni³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Since its launch on the market, Microsoft Kinect sensor has represented a great revolution in the field of low cost navigation, especially for indoor robotic applications. In fact, this system is endowed with a depth camera, as well as a visual RGB camera, at a cost of about 200$. The characteristics and the potentiality of the Kinect sensor have been widely studied for indoor applications. The second generation of this sensor has been announced to be capable of acquiring data even outdoors, under direct sunlight. The task of navigating passing from an indoor to an outdoor environment (and vice versa) is very demanding because the sensors that work properly in one environment are typically unsuitable in the other one. In this sense the Kinect could represent an interesting device allowing bridging the navigation solution between outdoor and indoor. In this work the accuracy and the field of application of the new generation of Kinect sensor have been tested outdoor, considering different lighting conditions and the reflective properties of the emitted ray on different materials. Moreover, an integrated system with a low cost GNSS receiver has been studied, with the aim of taking advantage of the GNSS positioning when the satellite visibility conditions are good enough. A kinematic test has been performed outdoor by using a Kinect sensor and a GNSS receiver and it is here presented.

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“…The Kinect sensor has been adopted in many indoor robotic applications, e.g. the Kinect sensor is utilized for 3D reconstruction and interaction based on GPU pipeline in [21], for tracking human hand articulation in [22] and for mobile robots navigation in [23]. A study about using Kinect for robotics applications is given in work [24].…”

Section: B Cameramentioning

confidence: 99%

Visual servoing for object manipulation: A case study in slaughterhouse

Andersen

et al. 2016

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Abstract-Automation for slaughterhouse challenges the design of the control system due to the variety of the objects. Realtime sensing provides instantaneous information about each piece of work and thus, is useful for robotic system developed for slaughterhouse. In this work, a pick and place task which is a common task among tasks in slaughterhouse is selected as the scenario for the system demonstration. A vision system is utilized to grab the current information of the object, including position and orientation. The information about the object is then transferred to the robot side for path planning. An online and offline combined path planning algorithm is proposed to generate the desired path for the robot control. An industrial robot arm is applied to execute the path. The system is implemented for a lab-scale experiment, and the results show a high success rate of object manipulation in the pick and place task. The approach is implemented in ROS which allows utilization of the developed algorithm on different platforms with little extra effort.

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Kinect v2 for mobile robot navigation: Evaluation and modeling

Cited by 261 publications

References 11 publications

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

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

Integration of Kinect and Low-Cost GNSS for Outdoor Navigation

Visual servoing for object manipulation: A case study in slaughterhouse

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