A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion. Figueroa, N., Dong, H., & El Saddik, A. ACM Trans. Intell. Syst. Technol., Association for Computing Machinery, New York, NY, USA, March, 2015.
Paper doi abstract bibtex 1 download We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.
@article{10.1145/2629673, author = {Figueroa, Nadia and Dong, Haiwei and El Saddik, Abdulmotaleb}, title = {A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion}, year = {2015}, issue_date = {May 2015}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {6}, number = {2}, issn = {2157-6904}, url = {https://doi.org/10.1145/2629673}, doi = {10.1145/2629673}, abstract = {We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.}, journal = {ACM Trans. Intell. Syst. Technol.}, month = mar, articleno = {14}, numpages = {10}, keywords = {Indoor mapping, kinect, benchmark datasets, evaluation} }
Downloads: 1
{"_id":"qEk4EQEYZZtShXwm5","bibbaseid":"figueroa-dong-elsaddik-acombinedapproachtowardconsistentreconstructionsofindoorspacesbasedon6drgbdodometryandkinectfusion-2015","downloads":1,"creationDate":"2018-01-25T15:52:37.745Z","title":"A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion","author_short":["Figueroa, N.","Dong, H.","El Saddik, A."],"year":2015,"bibtype":"article","biburl":"https://www.dropbox.com/s/x2bon6vqlc79ekc/FigueroaPublications.bib?dl=1","bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Figueroa"],"firstnames":["Nadia"],"suffixes":[]},{"propositions":[],"lastnames":["Dong"],"firstnames":["Haiwei"],"suffixes":[]},{"propositions":[],"lastnames":["El","Saddik"],"firstnames":["Abdulmotaleb"],"suffixes":[]}],"title":"A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion","year":"2015","issue_date":"May 2015","publisher":"Association for Computing Machinery","address":"New York, NY, USA","volume":"6","number":"2","issn":"2157-6904","url":"https://doi.org/10.1145/2629673","doi":"10.1145/2629673","abstract":"We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.","journal":"ACM Trans. Intell. Syst. Technol.","month":"March","articleno":"14","numpages":"10","keywords":"Indoor mapping, kinect, benchmark datasets, evaluation","bibtex":"@article{10.1145/2629673, author = {Figueroa, Nadia and Dong, Haiwei and El Saddik, Abdulmotaleb}, title = {A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion}, year = {2015}, issue_date = {May 2015}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {6}, number = {2}, issn = {2157-6904}, url = {https://doi.org/10.1145/2629673}, doi = {10.1145/2629673}, abstract = {We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.}, journal = {ACM Trans. Intell. Syst. Technol.}, month = mar, articleno = {14}, numpages = {10}, keywords = {Indoor mapping, kinect, benchmark datasets, evaluation} }\n\n","author_short":["Figueroa, N.","Dong, H.","El Saddik, A."],"key":"10.1145/2629673","id":"10.1145/2629673","bibbaseid":"figueroa-dong-elsaddik-acombinedapproachtowardconsistentreconstructionsofindoorspacesbasedon6drgbdodometryandkinectfusion-2015","role":"author","urls":{"Paper":"https://doi.org/10.1145/2629673"},"keyword":["Indoor mapping","kinect","benchmark datasets","evaluation"],"metadata":{"authorlinks":{"figueroa, n":"https://nbfigueroa.github.io/"}},"downloads":1},"search_terms":["combined","approach","toward","consistent","reconstructions","indoor","spaces","based","rgb","odometry","kinectfusion","figueroa","dong","el saddik"],"keywords":["indoor mapping","kinect","benchmark datasets","evaluation"],"authorIDs":["5a69fd4590a235916e0000b8","5df82cf7dc1981de0100008a","6weNhrK95SmkxE6z5"],"dataSources":["QscCmn2DGDfW7oiMm","kJNNQTBYdRiDNqEoj","Myci9twJQBkzgwjJt","CaGiEo4vZFJvt7iqr"]}