Stabilizing information-driven exploration for bearings-only SLAM using range gating

This paper examines the problem of information-driven exploration for the purposes of simultaneous localization and mapping (SLAM) with a bearings-only sensor. In another work, we have demonstrated that employing an information-driven approach to exploration with an extended Kalman filter (EKF) can drive the robot to locations in the world where filter updates are ill-conditioned and linearization constraints are violated, potentially destabilizing the filter, and increasing the probability of divergence from the true state estimate. In this paper, we demonstrate an information-driven approach to exploration that preserves the stability of the EKF and produces maps that are significantly more accurate than a conventional information-driven approach. Our method is based on range-gating observations so as to avoid potentially destabilizing updates. We provide simulated experimental results demonstrating the superior performance of our approach over simple outlier gating and over heuristic-driven exploration.

Authors

• Robert Sim

Keywords

• Simultaneous localization and mapping
• Robot sensing systems
• Sensor phenomena and characterization
• State estimation
• Uncertainty
• Computer science
• Kalman filters
• Stability
• Optimal control
• Robot control
• Range Gate
• Simulation Results
• Kalman Filter
• Linear Constraints
• Extended Kalman Filter
• Locations In The World
• Expected Value
• Local Optimum
• Simulation Environment
• Information Gain
• Information Maximum
• Exploratory Approach
• Regional Mapping
• Noise Model
• Motion Model
• Real Sense
• Status Updates
• Range Of Sensors
• Robot Pose
• Minimum Range
• Virtual Sensors
• Sensor Noise
• Landmark Localization
• Current Pose
• Bearings-only SLAM
• Exploration