The SLAM Confidence Trap
Sebastian Sansoni, Santiago Ram\'on Tosetti Sanz
TL;DR
This paper critiques the SLAM community's focus on benchmark scores, highlighting the need for principled uncertainty estimation to improve system reliability and consistency.
Contribution
It advocates for a paradigm shift towards prioritizing real-time, principled uncertainty estimation as a key success metric in SLAM systems.
Findings
Current SLAM systems are geometrically accurate but probabilistically inconsistent.
Prioritizing benchmark scores leads to brittle systems.
Real-time uncertainty estimation enhances SLAM robustness.
Abstract
The SLAM community has fallen into a "Confidence Trap" by prioritizing benchmark scores over principled uncertainty estimation. This yields systems that are geometrically accurate but probabilitistically inconsistent and brittle. We advocate for a paradigm shift where the consistent, real-time computation of uncertainty becomes a primary metric of success.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsConstraint Satisfaction and Optimization · Autonomous Vehicle Technology and Safety · Adversarial Robustness in Machine Learning