Nonlinear Optimization
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Large scale graph-based SLAM using aerial images as prior information
Autonomous Robots
On Kalman Filtering With Nonlinear Equality Constraints
IEEE Transactions on Signal Processing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
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When mapping is formulated in a Bayesian framework, the need of specifying a prior for the environment arises naturally. However, so far, the use of a particular structure prior has been coupled to working with a particular representation. We describe a system that supports inference with multiple priors while keeping the same dense representation. The priors are rigorously described by the user in a domain-specific language. Even though we work very close to the measurement space, we are able to represent structure constraints with the same expressivity as methods based on geometric primitives. This approach allows the intrinsic degrees of freedom of the environment's shape to be recovered. Experiments with simulated and real data sets will be presented.