16,230 research outputs found
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point
clouds. We propose an encoding of sparse 3D data from the Velodyne sensor
suitable for training a convolutional neural network (CNN). This general
purpose approach is used for segmentation of the sparse point cloud into ground
and non-ground points. The LiDAR data are represented as a multi-channel 2D
signal where the horizontal axis corresponds to the rotation angle and the
vertical axis the indexes channels (i.e. laser beams). Multiple topologies of
relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and
evaluated using a manually annotated dataset we prepared. The results show
significant improvement of performance over the state-of-the-art method by
Zhang et al. in terms of speed and also minor improvements in terms of
accuracy.Comment: ICRA 2018 submissio
Polarised light stress analysis and laser scatter imaging for non-contact inspection of heat seals in food trays
This paper introduces novel non-contact methods for detecting faults in heat seals of food packages. Two alternative imaging technologies are investigated; laser scatter imaging and polarised light stress images. After segmenting the seal area from the rest of the respective image, a classifier is trained to detect faults in different regions of the seal area using features extracted from the pixels in the respective region. A very large set of candidate features, based on statistical information relating to the colour and texture of each region, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating faults from non-faults. With this approach, different features can be selected and optimised for the different imaging methods. In experiments we compare the performance of classifiers trained using features extracted from laser scatter images only, polarised light stress images only, and a combination of both image types. The results show that the polarised light and laser scatter classifiers achieved accuracies of 96\% and 90\%, respectively, while the combination of both sensors achieved an accuracy of 95\%. These figures suggest that both systems have potential for commercial development
Semantic labeling of places using information extracted from laser and vision sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, kitchens,
offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly,
we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor
environments
Point cloud segmentation using hierarchical tree for architectural models
Recent developments in the 3D scanning technologies have made the generation
of highly accurate 3D point clouds relatively easy but the segmentation of
these point clouds remains a challenging area. A number of techniques have set
precedent of either planar or primitive based segmentation in literature. In
this work, we present a novel and an effective primitive based point cloud
segmentation algorithm. The primary focus, i.e. the main technical contribution
of our method is a hierarchical tree which iteratively divides the point cloud
into segments. This tree uses an exclusive energy function and a 3D
convolutional neural network, HollowNets to classify the segments. We test the
efficacy of our proposed approach using both real and synthetic data obtaining
an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201
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