65,150 research outputs found
Human-in-the-Loop SLAM
Building large-scale, globally consistent maps is a challenging problem, made
more difficult in environments with limited access, sparse features, or when
using data collected by novice users. For such scenarios, where
state-of-the-art mapping algorithms produce globally inconsistent maps, we
introduce a systematic approach to incorporating sparse human corrections,
which we term Human-in-the-Loop Simultaneous Localization and Mapping
(HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM
accepts approximate, potentially erroneous, and rank-deficient human input,
infers the intended correction via expectation maximization (EM),
back-propagates the extracted corrections over the pose graph, and finally
jointly optimizes the factor graph including the human inputs as human
correction factor terms, to yield globally consistent large-scale maps. We thus
contribute an EM formulation for inferring potentially rank-deficient human
corrections to mapping, and human correction factor extensions to the factor
graphs for pose graph SLAM that result in a principled approach to joint
optimization of the pose graph while simultaneously accounting for multiple
forms of human correction. We present empirical results showing the
effectiveness of HitL-SLAM at generating globally accurate and consistent maps
even when given poor initial estimates of the map.Comment: AAAI 201
Keyframe-based monocular SLAM: design, survey, and future directions
Extensive research in the field of monocular SLAM for the past fifteen years
has yielded workable systems that found their way into various applications in
robotics and augmented reality. Although filter-based monocular SLAM systems
were common at some time, the more efficient keyframe-based solutions are
becoming the de facto methodology for building a monocular SLAM system. The
objective of this paper is threefold: first, the paper serves as a guideline
for people seeking to design their own monocular SLAM according to specific
environmental constraints. Second, it presents a survey that covers the various
keyframe-based monocular SLAM systems in the literature, detailing the
components of their implementation, and critically assessing the specific
strategies made in each proposed solution. Third, the paper provides insight
into the direction of future research in this field, to address the major
limitations still facing monocular SLAM; namely, in the issues of illumination
changes, initialization, highly dynamic motion, poorly textured scenes,
repetitive textures, map maintenance, and failure recovery
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP
201
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