Application-oriented design space exploration for SLAM algorithms

In visual SLAM, there are many software and hardware parameters, such as algorithmic thresholds and GPU frequency, that need to be tuned; however, this tuning should also take into account the structure and motion of the camera. In this paper, we determine the complexity of the structure and motion with a few parameters calculated using information theory. Depending on this complexity and the desired performance metrics, suitable parameters are explored and determined. Additionally, based on the proposed structure and motion parameters, several applications are presented, including a novel active SLAM approach which guides the camera in such a way that the SLAM algorithm achieves the desired performance metrics. Real-world and simulated experimental results demonstrate the effectiveness of the proposed design space and its applications.

Authors

• Sajad Saeedi 0001
• Luigi Nardi
• Edward Johns
• Bruno Bodin
• Paul H. J. Kelly
• Andrew J. Davison

Keywords

• Simultaneous localization and mapping
• Algorithm design and analysis
• Measurement
• Software algorithms
• Space exploration
• Hardware
• Design Space
• Design Space Exploration
• Structural Parameters
• Performance Metrics
• Information Theory
• Motion Parameters
• Camera Motion
• Hardware Parameters
• Random Walk
• Image Intensity
• Kullback-Leibler
• Information Gain
• Algorithm Parameters
• Azimuth Angle
• Structure Of Space
• Path Planning
• Depth Images
• Robotic Arm
• Global Plan
• Information Divergence
• Pareto Front
• Camera Pose
• Maximum Divergence
• Iterative Closest Point
• Divergence Values
• Motion In Space
• Discrete Random Variable
• Divergence Distance
• Normal Vector