19 research outputs found
Supervised semantic labeling of places using information extracted from sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. 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 first 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 sensor range data 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. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
In depth-sensing applications ranging from home robotics to AR/VR, it will be
common to acquire 3D scans of interior spaces repeatedly at sparse time
intervals (e.g., as part of regular daily use). We propose an algorithm that
analyzes these "rescans" to infer a temporal model of a scene with semantic
instance information. Our algorithm operates inductively by using the temporal
model resulting from past observations to infer an instance segmentation of a
new scan, which is then used to update the temporal model. The model contains
object instance associations across time and thus can be used to track
individual objects, even though there are only sparse observations. During
experiments with a new benchmark for the new task, our algorithm outperforms
alternate approaches based on state-of-the-art networks for semantic instance
segmentation.Comment: IEEE International Conference on Computer Vision 201
Using Hierarchical EM to Extract Planes from 3D Range Scans
©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2005 IEEE International Conference on Robotics and Automation (ICRA), 18-22 April 2005, Barcelona, Spain.DOI: 10.1109/ROBOT.2005.1570803Recently, the acquisition of three-dimensional
maps has become more and more popular. This is motivated
by the fact that robots act in the three-dimensional world and
several tasks such as path planning or localizing objects can
be carried out more reliable using three-dimensional representations.
In this paper we consider the problem of extracting
planes from three-dimensional range data. In contrast to
previous approaches our algorithm uses a hierarchical variant
of the popular Expectation Maximization (EM) algorithm [1]
to simultaneously learn the main directions of the planar
structures. These main directions are then used to correct the
position and orientation of planes. In practical experiments
carried out with real data and in simulations we demonstrate
that our algorithm can accurately extract planes and their
orientation from range data
A new solution to map dynamic indoor environments
Author name used in this publication: G. Q. HuangAuthor name used in this publication: Y. K. Wong2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe