Inferring walls configuration of indoor environment could help robot "understand" the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.
Within GTSAM framework, @cite_7 uses door signs and walls as landmarks in SLAM. Door-signs are detected by a SVM-based classifier upon Histogram of Oriented Gradient (HOG) features. Walls are extracted from laser data using RANSAC. It is important to note that the SLAM works offline, i.e., it works after all observations data available. This is understandable regarding the SLAM works only with small number of landmarks (i.e., walls and door-signs) which is insufficient to make GTSAM framework works accurately online.
@cite_7
2102990061
Complex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the environment solely for the robot's benefit. Human environments contain many objects which can serve as suitable landmarks for robot navigation such as signs, objects, and furniture. Maps based on high level features which are identified by a learned classifier could better inform tasks such as semantic mapping and mobile manipulation. In this paper we present a technique for recognizing door signs using a learned classifier as one example of this approach, and demonstrate their use in a graphical SLAM framework with data association provided by reasoning about the semantic meaning of the sign. We describe a probabilistic framework for detection and modeling of doors from sensor data acquired in corridor environments with mobile robots. The framework captures shape, color, and motion properties of door and wall objects. The probabilistic model is optimized with a version of the expectation maximization algorithm, which segments the environment into door and wall objects and learns their properties. The framework allows the robot to generalize the properties of detected object instances to new object instances. We demonstrate the algorithm on real-world data acquired by a Pioneer robot equipped with a laser range finder and an omni-directional camera. Our results show that our algorithm reliably segments the environment into walls and doors, finding both doors that move and doors that do not move. We show that our approach achieves better results than models that only capture behavior, or only capture appearance. In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data. Traffic Sign Recognition (TSR) is an important component of Advanced Driver Assistance Systems (ADAS). The traffic signs enhance traffic safety by informing the driver of speed limits or possible dangers such as icy roads, imminent road works or pedestrian crossings. We present a three-stage real-time Traffic Sign Recognition system in this paper, consisting of a segmentation, a detection and a classification phase. We combine the color enhancement with an adaptive threshold to extract red regions in the image. The detection is performed using an efficient linear Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features. The tree classifiers, K-d tree and Random Forest, identify the content of the traffic signs found. A spatial weighting approach is proposed to improve the performance of the K-d tree. The Random Forest and Fisher's Criterion are used to reduce the feature space and accelerate the classification. We show that only a subset of about one third of the features is sufficient to attain a high classification accuracy on the German Traffic Sign Recognition Benchmark (GTSRB).