Search
Syndicate
Recent blog posts
Monthly archive
- March 2010 (67)
- February 2010 (116)
- January 2010 (114)
- December 2009 (121)
- November 2009 (51)
- October 2009 (18)
- September 2009 (7)
- July 2009 (20)
- June 2009 (58)
- May 2009 (17)
Experimental analysis of Harmonic Shape Images
In our early work, a representation called Harmonic Shape Images for 3D free-form surfaces was proposed and applied to solve the surface matching problem (D. Zhang and M. Hebert, 1999). Extensive experiments using real data are conducted to analyze the performance of Harmonic Shape Images with respect to discriminability, stability and robustness to resolution and occlusion. The results show that Harmonic Shape Images are an appropriate representation for 3D surface comparison. Examples of surface comparison using real data are presented.
Harmonic Maps and Their Applications in Surface Matching
The surface-matching problem is investigated in this
paper using a mathematical tool called harmonic maps.
The theory of harmonic maps studies the mapping
between different metric manifolds from the energyminimization
point of view. With the application of
harmonic maps, a surface representation called harmonic
shape images is generated to represent and match 3D freeform
surfaces.The basic idea of harmonic shape images is to map a 3D
surface patch with disc topology to a 2D domain and
encode the shape information of the surface patch into the
2D image.
On Generating Multi-resolution Representations of Polygonal Meshes
Multi-resolution representation is one of the effective approaches to image processing in both computer vision and computer graphics. However, the generation of multi-resolution representation for complex 3D polygonal mesh is a difficult problem. In this paper, a number of tools developed in our research group for this purpose are described. These tools employ different techniques such as smoothing, edge length normalization and wavelets to create representations which describe 3D shape at multi-resolutions.
Multi-Scale Classification of 3D Objects
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced. Given a library of models, objects that are similar based on this multi-scale measure are grouped together into classes.
Multi-Scale Classification of 3D Objects
We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. In a way similar to the classical scale space representations, larger amount of smoothing removes more details from the surfaces. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced.
Natural Terrain Classification using 3-D Ladar Data
Because of the difficulty of interpreting laser data in a meaningful way, safe navigation in vegetated terrain is still a daunting challenge. In this paper, we focus on the segmentation of ladar data using local 3-D point statistics into three classes: clutter to capture grass and tree canopy, linear to capture thin objects like wires or tree branches, and finally surface to capture solid objects like ground terrain surface, rocks or tree trunks. We present the details of the method proposed, the modifications we made to implement it on-board an autonomous ground vehicle.
Quality Assessment of Traversability Maps from Aerial LIDAR Data for an Unmanned Ground Vehicle
In this paper we address the problem of assessing quantitatively the quality of traversability maps computed from data collected by an airborne laser range finder. Such data is used to plan paths for an unmanned ground vehicle (UGV) prior to the execution of long range traverses. Little attention has been devoted to the problem we address in this paper. We use a unique data set of geodetic control points, real robot navigation data, ground LIDAR (LIght Detection And Ranging) data and aerial imagery, collected during a week long demonstration to support our work.
Experimental Results in Using Aerial LADAR Data for Mobile Robot Navigation
In this paper, we investigate the use of high resolution aerial LADAR data for autonomous mobile robot navigation in natural environments. The use of prior maps from aerial LADAR (LAser Detection And Ranging) survey is considered for enhancing system performance in two areas. First, the prior maps are used for registration with the data from the robot in order to compute accurate localization in the map. Second, the prior maps are used for computing detailed traversability maps that are used for planning over long distances.
Robust Extraction of Multiple Structures from Non-uniformly Sampled Data
The extraction of multiple coherent structures from point clouds is crucial to the problem of scene modeling. While many statistical methods exist for robust estimation from noisy data, they are inadequate for addressing issues of scale, semi-structured clutter, and large point density variation together with the computational restrictions of autonomous navigation. This paper extends an approach of nonparametric projection-pursuit based regression to compensate for the non-uniform and directional nature of data sampled in outdoor environments.
Evaluation of Image-Based Landmark Recognition Techniques
Recognizing landmarks in sequences of images is a challenging problem for a number of reasons. First
of all, the appearance of any given landmark varies substantially from one observation to the next.
