Online constraint network optimization for efficient maximum likelihood map learning

In this paper, we address the problem of incrementally optimizing constraint networks for maximum likelihood map learning. Our approach allows a robot to efficiently compute configurations of the network with small errors while the robot moves through the environment. We apply a variant of stochastic gradient descent and use a tree-based parameterization of the nodes in the network. By integrating adaptive learning rates in the parameterization of the network, our algorithm can use previously computed solutions to determine the result of the next optimization run. Additionally, our approach updates only the parts of the network which are affected by the newly incorporated measurements and starts the optimization approach only if the new data reveals inconsistencies with the network constructed so far. These improvements yield an efficient solution for this class of online optimization problems. Our approach has been implemented and tested on simulated and on real data. We present comparisons to recently proposed online and offline methods that address the problem of optimizing constraint network. Experiments illustrate that our approach converges faster to a network configuration with small errors than the previous approaches.

Authors

• Giorgio Grisetti
• Dario Lodi Rizzini
• Cyrill Stachniss
• Edwin Olson
• Wolfram Burgard

Keywords

• Constraint optimization
• Robotics and automation
• Simultaneous localization and mapping
• Computer networks
• Robots
• Stochastic processes
• Information filters
• USA Councils
• Adaptive systems
• Testing
• Optimal Network
• Network Constraints
• Learning Rate
• Gradient Descent
• Part Of Network
• Stochastic Gradient Descent
• Network Configuration
• Adaptive Rate
• Online Optimization
• Adaptive Learning Rate
• Efficient Algorithm
• Kalman Filter
• Equilibrium Point
• Cognitive Map
• Root Of The Tree
• Remainder Of This Section
• Fisher Information
• Particle Filter
• Online Methods
• Node Level
• Incremental Algorithm
• Robot Pose
• Odometry
• Treemap
• QR Decomposition
• Observation Likelihood
• Global Frame
• Updated Network
• Network Error