Succinct Landmark Database

Recently developed robotic mapping techniques enable the acquisition of large scale landmark databases. This paper explores an approach for succinct landmark database, which memorizes a large collection of point landmarks while allowing to random access the location of i-th landmark. Our approach combines and extends three independent compression techniques: space coding, succinct data structure, and exemplar-based scene compression. Experiments using real datasets evaluate effectiveness of the presented techniques in terms of compactness, access speed, and accuracy of landmark database.

[1]  Gonzalo Navarro,et al.  Directly Addressable Variable-Length Codes , 2009, SPIRE.

[2]  H. Sagan Space-filling curves , 1994 .

[3]  Ulrich Neumann,et al.  2.5D building modeling with topology control , 2011, CVPR 2011.

[4]  Florent Lafarge,et al.  Hybrid multi-view reconstruction by Jump-Diffusion , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Michael Wimmer,et al.  Instant architecture , 2003, ACM Trans. Graph..

[6]  Paul Newman,et al.  Accelerated appearance-only SLAM , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Ian D. Reid,et al.  Real-Time SLAM Relocalisation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Tanaka Kanji,et al.  An incremental scheme for dictionary-based compressive SLAM , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Frank Dellaert,et al.  Subgraph-preconditioned conjugate gradients for large scale SLAM , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Benoît Hudson,et al.  Succinct Representation of Well-Spaced Point Clouds , 2009, ArXiv.

[12]  Reinhard Klein,et al.  Eurographics Symposium on Point-based Graphics (2006) Octree-based Point-cloud Compression , 2022 .

[13]  Hans-Peter Seidel,et al.  Predictive point-cloud compression , 2005, SIGGRAPH '05.

[14]  Matthias Zwicker,et al.  Surfels: surface elements as rendering primitives , 2000, SIGGRAPH.

[15]  Liang-Tien Chia,et al.  Estimating camera pose from a single urban ground-view omnidirectional image and a 2D building outline map , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Tanaka Kanji,et al.  Dictionary-based map compression for sparse feature maps , 2011, 2011 IEEE International Conference on Robotics and Automation.

[17]  Philippe Bekaert,et al.  Self-Similarity-Based Compression of Point Clouds, with Application to Ray Tracing , 2007, PBG@Eurographics.

[18]  Tanaka Kanji,et al.  Dictionary-based map compression using geometric priors , 2011, ROBIO 2011.

[19]  Ulrich Neumann,et al.  A streaming framework for seamless building reconstruction from large-scale aerial LiDAR data , 2009, CVPR.

[20]  Jean-Arcady Meyer,et al.  Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.

[21]  Tanaka Kanji,et al.  Grammar-based map compression using Manhattan world priors , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.