Title:
Incremental Light Bundle Adjustment for Robotics Navigation
Incremental Light Bundle Adjustment for Robotics Navigation
Author(s)
Indelman, Vadim
Melim, Andrew
Dellaert, Frank
Melim, Andrew
Dellaert, Frank
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
This paper presents a new computationally-efficient method for vision-aided navigation (VAN) in autonomous robotic applications. While many VAN approaches
are capable of processing incoming visual observations, incorporating loop-closure measurements typically requires performing
a bundle adjustment (BA) optimization, that involves both all the past navigation states and the observed 3D points.
Our approach extends the incremental light bundle adjustment (LBA) method, recently developed for structure from motion [10], to information fusion in robotics navigation and
in particular for including loop-closure information. Since in many robotic applications the prime focus is on navigation
rather then mapping, and as opposed to traditional BA, we algebraically eliminate the observed 3D points and do not
explicitly estimate them. Computational complexity is further improved by applying incremental inference. To maintain high-rate performance over time, consecutive IMU measurements
are summarized using a recently-developed technique and
navigation states are added to the optimization only at camera
rate. If required, the observed 3D points can be reconstructed at
any time based on the optimized robot’s poses. The proposed
method is compared to BA both in terms of accuracy and
computational complexity in a statistical simulation study.
Sponsor
Date Issued
2013-11
Extent
Resource Type
Text
Resource Subtype
Post-print
Proceedings
Proceedings