Accurate Camera Pose Estimation for KinectFusion Based on Line Segment Matching by LEHF

Nakayama, Yusuke; Honda, Toshihiro; Saitō, Hideo; Shimizu, Masayoshi; Yamaguchi, Nobuyasu

doi:10.1109/icpr.2014.374

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…Therefore, the performance of point-feature-based algorithms in Kinect-based applications is not fully satisfactory. 23,24 Line-feature based or planar-feature based algorithms performed well in structured environments. However, they are not competent in unstructured natural scenes because it is extremely difficult to extract robust line or planar features in these scenes.…”

Section: Introductionmentioning

confidence: 96%

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

Yue

Chen

et al. 2016

J. Electron. Imaging

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Three-dimensional (3-D) simultaneous localization and mapping (SLAM) is a crucial technique for intelligent robots to navigate autonomously and execute complex tasks. It can also be applied to shape measurement, reverse engineering, and many other scientific or engineering fields. A widespread SLAM algorithm, named KinectFusion, performs well in environments with complex shapes. However, it cannot handle translation uncertainties well in highly structured scenes. This paper improves the KinectFusion algorithm and makes it competent in both structured and unstructured environments. 3-D line features are first extracted according to both color and depth data captured by Kinect sensor. Then the lines in the current data frame are matched with the lines extracted from the entire constructed world model. Finally, we fuse the distance errors of these linepairs into the standard KinectFusion framework and estimate sensor poses using an iterative closest pointbased algorithm. Comparative experiments with the KinectFusion algorithm and one state-of-the-art method in a corridor scene have been done. The experimental results demonstrate that after our improvement, the KinectFusion algorithm can also be applied to structured environments and has higher accuracy.Experiments on two open access datasets further validated our improvements.

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Section: Introductionmentioning

confidence: 96%

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

Yue

Chen

et al. 2016

J. Electron. Imaging

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“…Raposo [8] proposed several modifications to the work of Herrera [4] that improved runtime using less images. Nakayama [9] propose an alignment method which is based on line segments to improve the camera pose accuracy. However, all these methods are either not robust, or produce a result which is not accurate enough for real applications, or require relatively complex processing procedure.…”

Section: Introductionmentioning

confidence: 99%

RGB-D Sensors Calibration for Service Robots SLAM

Sun

Liu

Sun

2015

Lecture Notes in Computer Science

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Abstract. One of the major research directions in robotic vision focuses on SLAM using RGB-D sensors. The information can be used for decision making of robots and other areas that require precise position as a feature. This paper presents a novel algorithm to calibrate the RGB-D sensors for service robots SLAM. The distortions of the RGB and depth images are calibrated before the sensor is used as a measuring device for robot navigation. The calibration procedure includes the correction of the RGB and depth image as well as alignment of the RGB lens with the depth lens. The key advances in this paper are: a new method for RDB-D sensors calibration, and use of a depth distortion correcting model to help improve measurement precision. We experimentally verify our algorithm using varies of methods. The results show that, typically, our approach provides accurate calibration and the RGB-D sensors could provide reliable measurement information for robots navigating in unknown environments.

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“…However, if the descriptor distances from the two directions are similar, mismatching is occurred. To improve the matching performance, we proposed Directed LEHF [9]. We adopt Directed LEHF as a line segment feature descriptor.…”

Section: Matching 2d Line Segments By Directed Lehfmentioning

confidence: 99%

“…However, there is a fear that LC i contains some mismatches. We use a method which solves the PnL problem with an algorithm like RANSAC explained in [9], and estimate the camera pose RT i cw . This method mainly use RPnL [13] for solving the PnL problem.…”

Section: Solution For the Pnl Problemmentioning

confidence: 99%

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3D Line Segment Based Model Generation by RGB-D Camera for Camera Pose Estimation

Nakayama

Saitō

Shimizu

et al. 2015

Computer Vision - ACCV 2014 Workshops

Self Cite

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Abstract. In this paper, we propose a novel method for generating 3D line segment based model from an image sequence taken with a RGB-D camera. Constructing 3D geometrical representation by 3D model is essential for model based camera pose estimation that can be performed by corresponding 2D features in images with 3D features of the captured scene. While point features are mostly used as such features for conventional camera pose estimation, we aim to use line segment features for improving the performance of the camera pose estimation. In this method, using RGB images and depth images of two continuous frames, 2D line segments from the current frame and 3D line segments from the previous frame are corresponded. The 2D-3D line segment correspondences provide camera pose of the current frame. All of 2D line segments are finally back-projected to the world coordinate based on the estimated camera pose for generating 3D line segment based model of the target scene. In experiments, we confirmed that the proposed method can successfully generate line segment based models, while 3D models based on the point features often fail to successfully represent the target scene.

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Accurate Camera Pose Estimation for KinectFusion Based on Line Segment Matching by LEHF

Cited by 11 publications

References 14 publications

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

RGB-D Sensors Calibration for Service Robots SLAM

3D Line Segment Based Model Generation by RGB-D Camera for Camera Pose Estimation

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