Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Semi-Parametric Models for Visual OdometryThis paper introduces a novel framework for estimating the motion of a robotic car from image information (a.k.a. visual odometry). Most current monocular visual odometry algorithms rely on a calibrated camera model and recover relative rotation and translation by tracking image features and applying geometrical constraints. This approach has some drawbacks: translation is recovered up to a scale, it requires camera calibration, and uncertainty estimates are not directly obtained. We propose an alternative approach that involves the use of semi-parametric statistical models as means to recover scale, infer camera parameters and provide uncertainty estimates given a training dataset. As opposed to conventional non-parametric machine learning procedures, where standard models for egomotion would be neglected, we present a novel framework in which the existing parametric models and powerful non-parametric Bayesian learning procedures are combined. We devise a multiple output Gaussian Process procedure, named Coupled GP, that uses a parametric model as the mean function and a non-stationary covariance function to map image features directly into vehicle motion. Additionally, this procedure is also able to infer joint uncertainty estimates for rotation and translation. Experiments performed using data collected from a single camera under challenging conditions show that this technique outperforms traditional methods in trajectories of several kilometers.
Efficient On-Line Data Summarization Using Extremum SummariesWe are interested in the task of online summarization of the data observed by a mobile robot, with the goal that these summaries could be then be used for applications such as surveillance, identifying samples to be collected by a planetary rover, and site inspections to detect anomalies. In this paper, we pose the summarization problem as an instance of the well known k-center problem, where the goal is to identify k observations so that the maximum distance of any observation from a summary sample is minimized. We focus on the online version of the summarization problem, which requires that the decision to add an incoming observation to the summary be made instantaneously. Moreover, we add the constraint that only a finite number of observed samples can be saved at any time, which allows for applications where the selection of a sample is linked to a physical action such as rock sample collection by a planetary rover. We show that the proposed online algorithm has performance comparable to the offline algorithm when used with real world data.
Place Representation in Topological Maps Based on Bubble SpacePlace representation is a key element in topological maps. This paper presents bubble space - a novel representation for "places" (nodes) in topological maps. The novelties of this model are two-fold: First, a mathematical formalism that defines bubble space is presented. This formalism extends previously proposed bubble memory to accommodate two new variables -- varying robot pose and multiple features. Each bubble surface preserves the local $S^2-$metric relations of the incoming sensory data from the robot's viewpoint. Secondly, for learning and recognition, bubble surfaces can be transformed into bubble descriptors that are compact and rotationally invariant, while being computable in an incremental manner. The proposed model is evaluated with support vector machine based decision making in two different settings: first with a mobile robot placed in a variety of locations and secondly using benchmark visual data.
DP-FACT: Towards Topological Mapping and Scene Recognition with Color for Omnidirectional CameraTopological mapping and scene recognition problems are still challenging, especially for online realtime vision-based applications. We develop a hierarchical probabilistic model to tackle them using color information. This work is stimulated by our previous work  which defined a lightweight descriptor using color and geometry information from segmented panoramic images. Our novel model uses a Dirichlet Process Mixture Model to combine color and geometry features which are extracted from omnidirectional images. The inference of the model is based on an approximation of conditional probabilities of observations given estimated models. It allows online inference of the mixture model in real-time (at 50Hz), which outperforms other existing approaches. A real experiment is carried out on a mobile robot equipped with an omnidirectional camera. The results show the competence against the state-of-art.
Acquiring Semantics Induced Topology in Urban EnvironmentsMethods for acquisition and maintenance of an environment model are central to a broad class of mobility and navigation problems. Towards this end, various metric, topological or hybrid models have been proposed. Due to recent advances in sensing and recognition, acquisition of semantic models of the environments have gained increased interest in the community. In this work, we will demonstrate a capability of using weak semantic models of the environment to induce different topological models, capturing the spatial semantics of the environment at different levels. In the first stage of the model acquisition, we propose to compute semantic layout of the street scenes imagery by recognizing and segmenting buildings, roads, sky, cars and trees. Given such semantic layout, we propose an informative feature characterizing the layout and train a classifier to recognize street intersections in challenging urban inner city scenes. We also show how the evidence of different semantic concepts can induce useful topological representation of the environment, which can aid navigation and localization tasks. To demonstrate the approach, we carry out experiments on a challenging dataset of omnidirectional inner city street views and report the performance of both semantic segmentation and intersection classification.
Large-scale Semantic Mapping and Reasoning with Heterogeneous ModalitiesThis paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chain-graph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space.