Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision

Wasenmüller, Oliver; Stricker, Didier

doi:10.1007/978-3-319-54427-4_3

Cited by 162 publications

(144 citation statements)

References 21 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…Fuersattel et al evaluated multiple time-of-flight cameras with respect to different error sources [7]. Wasenmüller and Stricker compare the structured light Kinect V1 camera to the time-of-flight-based Kinect V2 camera [8].…”

Section: Introductionmentioning

confidence: 99%

Accurate laser scanner to camera calibration with application to range sensor evaluation

Fuersattel

Plank

Maier

et al. 2017

IPSJ T Comput Vis Appl

View full text Add to dashboard Cite

Multi-modal sensory data plays an important role in many computer vision and robotics tasks. One popular multi-modal pair is cameras and laser scanners. To overlay and jointly use the data from both modalities, it is necessary to calibrate the sensors, i.e., to obtain the spatial relation between the sensors. Computing such a calibration is challenging as both sensors provide quite different data: cameras yield color or brightness information, laser scanners yield 3-D points. However, several laser scanners additionally provide reflectances, which turn out to make calibration to a camera well feasible. To this end, we first estimate a rough alignment of the coordinate systems of both modalities. Then, we use the laser scanner reflectances to compute a virtual image of the scene. Stereo calibration on the virtual image and the camera image are then used to compute a refined, high-accuracy calibration. It is encouraging that the accuracies in our experiments are comparable to camera-camera stereo setups and outperform another of other target-based calibration approach. This shows that the proposed algorithm reliably integrates the point cloud with the intensity image. As an example application, we use the calibration results to obtain ground-truth distance images for range cameras. Furthermore, we utilize this data to investigate the accuracy of the Microsoft Kinect V2 time-of-flight and the Intel RealSense R200 structured light camera.

show abstract

Section: Introductionmentioning

confidence: 99%

Accurate laser scanner to camera calibration with application to range sensor evaluation

Fuersattel

Plank

Maier

et al. 2017

IPSJ T Comput Vis Appl

View full text Add to dashboard Cite

show abstract

“…But the accuracy of Kinect is not suitable for robotic grasp. In [10], the depth accuracy is evaluated among other characteristics. Stereo vision camera can provide better depth accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Object Detection and Pose Estimation Approach for Position Based Visual Servoing

Shi

2017

Electrical, Control and Communication Engineering

View full text Add to dashboard Cite

-In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.

show abstract

“…Until now many studies on the Kinect v2 and its various applications have been carried out, such as a comparison between the two generations of Microsoft sensors-Kinect v1 and v2 [17,19,20], 3D reconstruction [3,7,13,21], and mobile robot navigation [14]. Within this section, the work related to the above is overviewed.…”

Section: Related Workmentioning

confidence: 99%

“…The Kinect v2 uses time-of-flight technology for measuring distances [20]. From the physical and mathematical point of view, Valgma et al [20] analyze the depth-measuring principle in detail.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes

Jiao

Yuan

Tang

et al. 2017

IJGI

View full text Add to dashboard Cite

3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods.

show abstract

Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision

Cited by 162 publications

References 21 publications

Accurate laser scanner to camera calibration with application to range sensor evaluation

Accurate laser scanner to camera calibration with application to range sensor evaluation

An Object Detection and Pose Estimation Approach for Position Based Visual Servoing

A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes

Contact Info

Product

Resources

About