The computation of optical flow
ACM Computing Surveys (CSUR)
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Fuzzy Adaptive Turbulent Particle Swarm Optimization
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments
Robotics and Autonomous Systems
A framework for robust and incremental self-localization of a mobile robot
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Object tracking using genetic evolution based kernel particle filter
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Robotics
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
The EvA2 optimization framework
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Petri-net-based implementations for FIRA weightlifting and sprint games with a humanoid robot
Robotics and Autonomous Systems
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The localization of mobile systems with video data is a challenging field in robotic vision research. Apart from support technologies like a GPS, a self-sufficient visual system is desirable. We introduce a new heuristic approach to outdoor localization in a scenario with sparse visual data and without odometry readings. Localization is interpreted as an optimization problem, and a swarm-based optimization method is adapted and applied, remaining independent of the specific visual feature type. The new method obtains similar or better localization results in our experiments while requiring only two-thirds of the number of image comparisons, indicating an overall speed-up by 25%.