Plane Detection in Point Cloud Data

Plane detection is a prerequisite to a wide variety of vision tasks. RANdomSAmple Consensus (RANSAC) algorithm is widely used for plane detectionin point cloud data. Minimum description length (MDL) principle is used todeal with several competing hypothesis. This paper presents a new approachto the plane detection by integrating RANSAC and MDL. The method couldavoid detecting wrong planes due to the complex geometry of the 3D data.The paper tests the performance of proposed method on both synthetic andreal data

Graph-based segmentation of range data with applications to 3D urban mapping

This paper presents an efficient graph-based algorithm for the segmentation of planar regions out of 3D range maps of urban areas. Segmentation of planar surfaces in urban scenarios is challenging because the data acquired is typically sparsely sampled, incomplete, and noisy. The algorithm is motivated by Felzenszwalb’s algorithm to 2D image segmentation [8], and is extended to deal with non-uniformly sampled 3D range data using an approximate nearest neighbor search. Interpoint distances are sorted in increasing order and this list of distances is traversed growing planar regions that satisfy both local and global variation of distance and curvature. The algorithm runs in O(n log n) and compares favorably with other region growing mechanisms based on Expectation Maximization. Experiments carried out with real data acquired in an outdoor urban environment demonstrate that our approach is well-suited to segment planar surfaces from noisy 3D range data. A pair of applications of the segmented results are shown, a) to derive traversability maps, and b) to calibrate a camera network.Peer ReviewedPostprint (published version

A Robust Localization System for Inspection Robots in Sewer Networks †

Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network. It is capable of sensing gas concentrations and detecting failures in the network such as cracks and holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely geo-localized to allow the operators performing the required correcting measures. To this end, this paper presents a robust localization system for global pose estimation on sewers. It makes use of prior information of the sewer network, including its topology, the different cross sections traversed and the position of some elements such as manholes. The system is based on a Monte Carlo Localization system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Additionally, the localization is further refined with novel updating steps proposed in this paper which are activated whenever a discrete element in the sewer network is detected or the relative orientation of the robot over the sewer gallery could be estimated. Each part of the system has been validated with real data obtained from the sewers of Barcelona. The whole system is able to obtain median localization errors in the order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2

ToF cameras for active vision in robotics

ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited. After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/ foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.This work was supported by the EU project GARNICS FP7-247947, by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510, and by the Catalan Research Commission through SGR-00155Peer Reviewe