4,524 research outputs found
Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering
This paper investigates the use of depth images as localisation sensors for
3D map building. The localisation information is derived from the 3D data
thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the
ICP, and thus of the localization error, is analysed, and described by a Fisher
Information Matrix. It is advocated this error can be much reduced if the data
is fused with measurements from other motion sensors, or even with prior
knowledge on the motion. The data fusion is performed by a recently introduced
specific extended Kalman filter, the so-called Invariant EKF, and is directly
based on the estimated covariance of the ICP. The resulting filter is very
natural, and is proved to possess strong properties. Experiments with a Kinect
sensor and a three-axis gyroscope prove clear improvement in the accuracy of
the localization, and thus in the accuracy of the built 3D map.Comment: Submitted to IROS 2012. 8 page
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Invariant EKF Design for Scan Matching-aided Localization
Localization in indoor environments is a technique which estimates the
robot's pose by fusing data from onboard motion sensors with readings of the
environment, in our case obtained by scan matching point clouds captured by a
low-cost Kinect depth camera. We develop both an Invariant Extended Kalman
Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based
solution to this problem. The two designs are successfully validated in
experiments and demonstrate the advantage of the IEKF design
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
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