(Georgia Institute of Technology, 2015)
Indelman, Vadim; Roberts, Richard; Dellaert, Frank
Bundle adjustment (BA) is essential in many robotics and structure-from-motion applications. In robotics, often a bundle adjustment solution
is desired to be available incrementally as new poses and 3D points are
observed. Similarly in batch structure from motion, cameras are typically
added incrementally to allow good initializations. Current incremental
BA methods quickly become computationally expensive as more camera
poses and 3D points are added into the optimization. In this paper we
introduce incremental light bundle adjustment (iLBA), an efficient optimization framework that substantially reduces computational complexity compared to incremental bundle adjustment. First, the number of variables
in the optimization is reduced by algebraic elimination of observed
3D points, leading to a structureless BA. The resulting cost function is formulated
in terms of three-view constraints instead of re-projection errors
and only the camera poses are optimized. Second, the optimization problem
is represented using graphical models and incremental inference is applied,
updating the solution using adaptive partial calculations each time a
new camera is incorporated into the optimization. Typically, only a small
fraction of the camera poses are recalculated in each optimization step.
The 3D points, although not explicitly optimized, can be reconstructed
based on the optimized camera poses at any time. We study probabilistic
and computational aspects of iLBA and compare its accuracy against incremental
BA and another recent structureless method using real-imagery
and synthetic datasets. Results indicate iLBA is 2-10 times faster than
incremental BA, depending on number of image observations per frame.
(Georgia Institute of Technology, 2014)
Williams, Stephen; Indelman, Vadim; Kaess, Michael; Roberts, Richard; Leonard, John J.; Dellaert, Frank
We present a parallelized navigation architecture that is capable of
running in real-time and incorporating long-term loop closure constraints
while producing the optimal Bayesian solution. This architecture splits
the inference problem into a low-latency update that incorporates new
measurements using just the most recent states (filter), and a high-latency
update that is capable of closing long loops and smooths using all past
states (smoother). This architecture employs the probabilistic graphical
models of Factor Graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the
full joint density is employed that allows the different inference operations
to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous
nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations
have completed. This architecture has been tested within a simulated
aerial environment and on real data collected from an autonomous ground
vehicle. In all cases, the concurrent architecture is shown to recover the
full batch solution, even while updated state estimates are produced in
real-time.