Corrective gradient refinement for mobile robot localization

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles.

Authors

• Joydeep Biswas
• Brian Coltin
• Manuela Veloso

Keywords

• Computational modeling
• Proposals
• Sensors
• Robots
• Accuracy
• Vectors
• Three dimensional displays
• Mobile Robot
• Robot Localization
• Mobile Robot Localization
• State Space
• Point Cloud
• Computational Requirements
• Local Algorithm
• Depth Camera
• Particle Filter
• Vector Map
• Fewer Particles
• Refinement Algorithm
• Error Of The Mean
• Markov Chain Monte Carlo
• Time Complexity
• Detailed Algorithm
• Motion Model
• 3D Point
• Final Distribution
• Importance Weights
• 2D Point
• Ray Casting
• Robot Pose
• 3D Point Cloud
• Stage Distribution
• LiDAR Scans
• Point Cloud Generation
• Update Step
• Simultaneous Localization And Mapping
• Hamiltonian Dynamics