Yanyan Lu

α-Decomposition of polygons

Abstract Decomposing a shape into visually meaningful parts comes naturally to humans, but recreating this fundamental operation in computers has been shown to be difficult. Similar challenges have puzzled researchers in shape reconstruction for decades. In this paper, we recognize the strong connection between shape reconstruction and shape decomposition at a fundamental level and propose a method called α - decomposition . The α - decomposition generates a space of decompositions parameterized by α , the diameter of a circle convolved with the input polygon. As we vary the value of α , some structural features appear and disappear quickly while others persist. Therefore, by analyzing the persistence of the features, we can determine better decompositions that are more robust to both geometrical and topological noises.

Online collision prediction among 2D polygonal and articulated obstacles

Collision prediction is a fundamental operation for planning motion in dynamic environments. Existing methods usually exploit complex behavior models or use dynamic constraints in collision prediction. However, these methods all assume simple geometry, such as a disc, which significantly limits their applicability. This paper proposes a new approach that advances collision prediction beyond disc robots and handles arbitrary polygons and articulated objects. Our new tool predicts collision by assuming that obstacles are adversarial. Comparing to an online motion planner that replans periodically at fixed time intervals and a planner that approximates obstacle with discs, our experimental results provide strong evidence that the new method significantly reduces the number of replans while maintaining a higher success rate of finding a valid path. Our geometric-based collision prediction method provides a tool for handling highly complex shapes and provides an approach complementary to those methods that consider behavior and dynamic constraints of objects with simple shapes.

Creating building ground plans via robust K -way union: A step toward large-scale simulation in urban environment

Due to collaborative efforts and advances in data acquisition technology, a large volume of geometric models describing urban buildings has become available in public domain via “Digital Earth” software like ESRI ArcGlobe and Google Earth. As a consequence, almost every major international city has been reconstructed in the virtual world. Although mostly created for visualization, we believe that these urban models can benefit many applications beyond visualization including video games, city scale evacuation plans, traffic simulations, and earth phenomenon simulations. However, before these urban models can be used in these applications, they require tedious manual preparation that usually takes weeks, if not months. In this paper, we present a framework that produces disjoint 2D ground plans from these urban models, an important step in the preparation process. Designing algorithms that can robustly and efficiently handle unstructured urban models at city scale is the main technical challenge. In this work, we show both theoretically and empirically that our method is resolution complete, efficient, and numerically stable.

Finding critical changes in dynamic configuration spaces

Given a motion planning problem in a dynamic but fully known environment, we propose the first roadmap-based method, called critical roadmap, that has the ability to identify and exploit the critical topological changes of the free configuration space. Comparing to the existing methods that either ignore temporal coherence or only repair their roadmaps at fixed times, our method provides not only a more complete representation of the free (configuration-time) space but also provides significant efficiency improvement. Our experimental results show that the critical roadmap method has a higher chance of finding solutions, and it is at least one order of magnitude faster than some well-known planners.

Collision Prediction Among Rigid and Articulated Obstacles with Unknown Motion

Collision prediction is a fundamental operation for planning motion in dynamic environment. Existing methods usually exploit complex behavior models or use dynamic constraints in collision prediction. However, these methods all assume simple geometry, such as disc, which significantly limit their applicability. This paper proposes a new approach that advances collision prediction beyond disc robots and handles arbitrary polygons and articulated objects. Our new tool predicts collision by assuming that obstacles are adversarial. Comparing to an online motion planner that replans periodically at fixed time interval and planner that approximates obstacle with discs, our experimental results provide strong evidences that the new method significantly reduces the number of replans while maintaining higher success rate of finding a valid path. Our geometric-based collision prediction method provides a tool to handle highly complex shapes and provides a complimentary approach to those methods that consider behavior and dynamic constraints of objects with simple shapes.

Collision prediction among polygons with arbitrary shape and unknown motion.

Collision prediction is a fundamental operation for planning motion in dynamic environment. Existing methods usually exploit complex behavior models or use dynamic constraints in collision prediction. However, these methods all assume simple geometries, such as disc, which significantly limit their applicability. This paper proposes a new approach that advances collision prediction beyond disc robots and handles arbitrary polygons. Our new tool predicts collision by assuming that obstacles are adversarial. Comparing to an online motion planner that replans periodically at fixed time interval and planner that approximates obstacle with discs, our experimental results provide strong evidences that the new method significantly reduces the number of replans while maintaining higher success rate of finding a valid path. Our geometric-based collision prediction method provides a tool to handle highly complex shapes and provides a complimentary approach to those methods that consider behavior and dynamic constraints of objects with simple shapes.

Collision Prediction: Conservative Advancement Among Obstacles With Unknown Motion

Collision detection is a fundamental geometric tool for sampling-based motion planners. On the contrary, collision prediction for the scenarios that obstacle’s motion is unknown is still in its infancy. This paper proposes a new approach to predict collision by assuming that obstacles are adversarial. Our new tool advances collision prediction beyond the translational and disc robots; arbitrary polygons with rotation can be used to better represent obstacles and provide tighter bound on predicted collision time. Comparing to an online motion planner that replans periodically at fixed time interval, our experimental results provide strong evidences that our method significantly reduces the number of re-plannings while maintaining higher success rate of finding a valid path.Copyright

City-scale urban transport and dispersion simulation using geographic information system footprints

A large volume of urban models describing urban objects in major international cities has been re-constructed and become freely and publicly available via software like Arc-Globe and Google Earth. However, these models are mostly created for visualization and are loosely structured. For example, current GIS software such as ESRI ArcGIS and urban model synthesis methods typically use overlapping 2D footprints with elevation and height information to depict various components of buildings. In this paper, we present a robust and efficient framework that generates seamless 3D architectural models from these footprints that usually contain small, sharp, and various (nearly) degenerate artifacts due to machine and human errors. We demonstrate the benefits of the proposed method by showcase an atmospheric dispersion simulation in a New York City (NYC) dataset. Finally, we discuss several examples of visualizing and analyzing the simulated Computational Fluid Dynamics (CFD) data into the GIS for further geospatial analysis.

SMI 2012: Full α-Decomposition of polygons

Abstract Decomposing a shape into visually meaningful parts comes naturally to humans, but recreating this fundamental operation in computers has been shown to be difficult. Similar challenges have puzzled researchers in shape reconstruction for decades. In this paper, we recognize the strong connection between shape reconstruction and shape decomposition at a fundamental level and propose a method called α - decomposition . The α - decomposition generates a space of decompositions parameterized by α , the diameter of a circle convolved with the input polygon. As we vary the value of α , some structural features appear and disappear quickly while others persist. Therefore, by analyzing the persistence of the features, we can determine better decompositions that are more robust to both geometrical and topological noises.

α-Decompositionα-Decomposition of polygons ☆

Abstract Decomposing a shape into visually meaningful parts comes naturally to humans, but recreating this fundamental operation in computers has been shown to be difficult. Similar challenges have puzzled researchers in shape reconstruction for decades. In this paper, we recognize the strong connection between shape reconstruction and shape decomposition at a fundamental level and propose a method called α - decomposition . The α - decomposition generates a space of decompositions parameterized by α , the diameter of a circle convolved with the input polygon. As we vary the value of α , some structural features appear and disappear quickly while others persist. Therefore, by analyzing the persistence of the features, we can determine better decompositions that are more robust to both geometrical and topological noises.