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In this paper we propose a hybrid vision-based SLAM and moving objects tracking (vSLAMMOT) approach. This approach tightly combines two key methods: a superpixel-based segmentation to detect moving objects and a Rao-Blackwellized Particle Filter to estimate a stereo-vision-based SLAM posterior. Most successful methods perform vision-based SLAM (vSLAM) and track moving objects independently. However,...
In this paper, we present a particle-filter-based localization framework with decoupled visual measurements (image features) for process and measurement models. Thus our approach enables using camera-based motion estimation while achieving the independence between the process noise and the measurement noise in the Bayesian filtering framework. In addition, we alternately perform sequential and global...
In this paper, we propose a Markov Chain Monte Carlo (MCMC) sampling method with the data-driven proposal distribution for six-degree-of-freedom (6-DoF) SLAM. Recently, visual odometry priors have been widely used as the process model in the SLAM formulation to improve the SLAM performance. However, modeling the uncertainties of incremental motions estimated by visual odometry is especially difficult...
In this paper, we present a vision-based metric/topological approach for the Simultaneous Localization and Mapping (SLAM) problem. Similar to recent works on metric/topological SLAM (MT SLAM) where the map is represented as a graph whose nodes are submaps and edges are relative transformations between submaps, we propose a graphical model for vision-based MT SLAM that estimates the hybrid robot pose...
Vision-based robotic applications such as Simultaneous Localization and Mapping (SLAM), global localization, and autonomous navigation have suffered from problems related to dynamic environments involving moving objects and kidnapping. One of the possible solutions to these problems is to establish robust correspondences when obtaining images from static scenes. Therefore we propose an efficient technique...
Recently, many vision-based robotic applications such as visual SLAM (Simultaneous Localization And Mapping) and autonomous navigation have achieved good performance using visual features. In these applications, robust feature tracking plays an important role, e.g., in scene recognition for autonomous navigation and in data association for visual SLAM. In this paper, we propose a hierarchical outlier...
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