27 research outputs found
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
Index Terms: robotics, hand-eye problem, self-calibration, structure from
motio
BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization
Typical algorithms for point cloud registration such as Iterative Closest
Point (ICP) require a favorable initial transform estimate between two point
clouds in order to perform a successful registration. State-of-the-art methods
for choosing this starting condition rely on stochastic sampling or global
optimization techniques such as branch and bound. In this work, we present a
new method based on Bayesian optimization for finding the critical initial ICP
transform. We provide three different configurations for our method which
highlights the versatility of the algorithm to both find rapid results and
refine them in situations where more runtime is available such as offline map
building. Experiments are run on popular data sets and we show that our
approach outperforms state-of-the-art methods when given similar computation
time. Furthermore, it is compatible with other improvements to ICP, as it
focuses solely on the selection of an initial transform, a starting point for
all ICP-based methods.Comment: IEEE International Conference on Robotics and Automation 202
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Kalman filters are routinely used for many data fusion applications including
navigation, tracking, and simultaneous localization and mapping problems.
However, significant time and effort is frequently required to tune various
Kalman filter model parameters, e.g. process noise covariance, pre-whitening
filter models for non-white noise, etc. Conventional optimization techniques
for tuning can get stuck in poor local minima and can be expensive to implement
with real sensor data. To address these issues, a new "black box" Bayesian
optimization strategy is developed for automatically tuning Kalman filters. In
this approach, performance is characterized by one of two stochastic objective
functions: normalized estimation error squared (NEES) when ground truth state
models are available, or the normalized innovation error squared (NIS) when
only sensor data is available. By intelligently sampling the parameter space to
both learn and exploit a nonparametric Gaussian process surrogate function for
the NEES/NIS costs, Bayesian optimization can efficiently identify multiple
local minima and provide uncertainty quantification on its results.Comment: Final version presented at FUSION 2018 Conference, Cambridge, UK,
July 2018 (submitted June 1, 2018