(Georgia Institute of Technology, 2013-08)
Indelman, Vadim; Williams, Stephen; Kaess, Michael; Dellaert, Frank
This paper presents a new approach for high-rate information fusion in modern
inertial navigation systems, that have a variety of sensors operating at different
frequencies. Optimal information fusion corresponds to calculating the maximum a posteriori estimate over the joint probability distribution function (pdf) of all states, a computationally-expensive process in the general case. Our approach consists of two key components, which yields a flexible, high-rate,
near-optimal inertial navigation system. First, the joint pdf is represented using
a graphical model, the factor graph, that fully exploits the system sparsity and provides a plug and play capability that easily accommodates the addition and removal of measurement sources. Second, an efficient incremental inference
algorithm over the factor graph is applied, whose performance approaches
the solution that would be obtained by a computationally-expensive batch optimization
at a fraction of the computational cost. To further aid high-rate
performance, we introduce an equivalent IMU factor based on a recently developed
technique for IMU pre-integration, drastically reducing the number of
states that must be added to the system. The proposed approach is experimentally
validated using real IMU and imagery data that was recorded by a ground
vehicle, and a statistical performance study is conducted in a simulated aerial scenario. A comparison to conventional fixed-lag smoothing demonstrates that our method provides a considerably improved trade-off between computational complexity and performance.
(Georgia Institute of Technology, 2013-04)
Wu, Allen D.; Johnson, Eric N.; Kaess, Michael; Dellaert, Frank; Chowdhary, Girish
A vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied
environments is presented. Particularly, feature point information from a monocular vision sensor are used
to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial
Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing
the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of
a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When
GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position
and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS
scenario verify the presented method.