Khái niệm cốt lõi
Optimizing sensor placement in SLAM using information-theoretic criteria.
Tóm tắt
This article explores the impact of sensor arrangement on robotic perception, focusing on simultaneous localization and mapping (SLAM). It introduces OASIS, a method for optimal sensor design based on subset selection under the E-optimality criterion. The study shows that OASIS outperforms standard configurations in visual SLAM estimates through synthetic experiments. The paper also discusses related work, problem formulation, fast approximation algorithms, and evaluation results comparing greedy and convex relaxation approaches.
Structure:
- Introduction to Sensor Placement in Robotics
- Methodology: OASIS for Optimal Sensor Design
- Related Work on Sensor Architecture Design
- Problem Formulation as Subset Selection for SLAM
- Fast Approximation Algorithms: Greedy vs Convex Relaxation
- Evaluation Results and Comparison with Benchmarks
Thống kê
Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks.
The greedy method finds high-quality solutions within 1-2% suboptimality.
The convex relaxation approach provides an upper bound on optimal value.
Objective function fE is concave over the domain [0, 1]N.
Trích dẫn
"OASIS outperforms standard configurations in terms of mean squared error of visual SLAM estimates."
"Our proposed methodology formalizes the design task as an optimal subset selection problem."
"The greedy method succeeds in finding solutions that are within 1-2% suboptimal."