RANSAC: Identification of Higher-Order Geometric Features and Applications in Humanoid Robot Soccer
The ability for an autonomous agent to self-localise is directly proportional to the accuracy and precision with which it can perceive salient features within its local environment. The identification of such features by recognising geometric profile allows robustness against lighting variations, which is necessary in most industrial robotics applications. This paper details a framework by which the random sample consensus (RANSAC) algorithm, often applied to parameter fitting in linear models, can be extended to identify higher-order geometric features. Goalpost identification within humanoid robot soccer is investigated as an application, with the developed system yielding an order-of-magnitude improvement in classification performance relative to a traditional histogramming methodology.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
Geometric Polynomial Constraints in Higher-Order Graph Matching
Correspondence is a ubiquitous problem in computer vision and graph matching has been a natural way to formalize correspondence as an optimization problem. Recently, graph matching solvers have included higher-order term…
Graph MatchingUse of two Public Distributed Ledgers to track the money of an economy
A tool to improve the effectiveness and the efficiency of public spending is proposed here. In the 19th century banknotes had a serial number. However, in modern days the use of digital transactions that do not use physi…
SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a unified framework combining hyperspherical representation learning with struct…
Representation LearningNon-planar Object Detection and Identification by Features Matching and Triangulation Growth
Object detection and identification is surely a fundamental topic in the computer vision field; it plays a crucial role in many applications such as object tracking, industrial robots control, image retrieval, etc. We pr…
Image RetrievalIndustrial Robotsobject-detectionObject Detection+1ChainNet: Learning on Blockchain Graphs with Topological Features
With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other finan…
Graph Representation LearningRepresentation Learning