2D Pose Nodes Sampling Heuristic For Fast Loop Closing

다국어 초록 (Multilingual Abstract)

Graph SLAM approach has been gaining much attention in SLAM research recently thanks to its ability to provide better map and full trajectory estimation when compared to filter based SLAM approaches. Even though graph SLAM requires batch processing it to be comparatively computationally expensive, recent advancements in optimization and computing power enable it to run fast enough to be used even in real-time. However, data association problem still requires much of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck in SLAM algorithm. Our approach to alleviate this bottleneck is to sample limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. LOOP CLOSURE
• 3. SCAN MATCHING
• 4. POSE NODE SCORING
• 5. EXPERIMENT
• 6. AVERAGE LOOP CLOSING OPERATION TIME
• 7. EXPERIMENT RESULTS
• 9. CONCLUSION
• References