2 research outputs found
Metric Learning for Generalizing Spatial Relations to New Objects
Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.Comment: Accepted at the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems. The new Freiburg Spatial Relations Dataset and a demo
video of our approach running on the PR-2 robot are available at our project
website: http://spatialrelations.cs.uni-freiburg.d
A Statistical Measure for Map Consistency in SLAM.
Abstract-Map consistency is an important requirement for applications in which mobile robots need to effectively perform autonomous navigation tasks. While recent SLAM techniques provide an increased robustness even in the context of bad initializations or data association outliers, the question of how to determine whether or not the resulting map is consistent is still an open problem. In this paper, we introduce a novel measure for map consistency. We compute this measure by taking into account the discrepancies in the sensor data and leverage it to address two important problems in SLAM. First, we derive a statistical test for assessing whether a map is consistent or not. Second, we employ it to automatically set the free parameter of dynamic covariance scaling, a robust SLAM back-end. We present an evaluation of our approach on over 50 maps sourced from 16 publicly available datasets and illustrate its capability for the inconsistency detection and the tuning of the parameter of the back-end