Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

The SLAM Confidence Trap

Sebastian Sansoni, Santiago Ramón Tosetti Sanz

TL;DR

This work advocates for a paradigm shift where the consistent, real-time computation of uncertainty becomes a primary metric of success in SLAM.

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.

The SLAM Confidence Trap

TL;DR

This work advocates for a paradigm shift where the consistent, real-time computation of uncertainty becomes a primary metric of success in SLAM.

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.
Paper Structure (11 sections, 1 figure)

This paper contains 11 sections, 1 figure.

Table of Contents

  1. INTRODUCTION
  2. THE STATE OF THE ART: A TALE OF LOST COVARIANCE
  3. The Probabilistic Era (Pre-2010s)
  4. The Optimization Revolution: A Paradigm Shift
  5. The Machine Learning Renaissance
  6. The Resulting Evaluation Gap
  7. Emerging Probabilistic and Decentralized Paradigms
  8. The Case for Uncertainty-Awareness: From Passive Metric to Active Driver
  9. Modern Tools for Uncertainty Quantification
  10. DISCUSSION AND FUTURE DIRECTIONS
  11. CONCLUSIONS

Figures (1)

  • Figure 1: An illustration of the historical shift in SLAM research focus, depicting the relative decline in explicit uncertainty estimation. The graph plots the proportion of publications over time that mention key terms related to: (i) filter-based methods (e.g., EKF, particle filters), (ii) optimization-based methods (e.g., graph optimization, bundle adjustment), and (iii) uncertainty or covariance. A clear trend emerges post-2010, coinciding with the "Raise of Benchmarks", where optimization-based approaches dominate while the relative focus on uncertainty estimation diminishes. This divergence highlights the emergence of "the Confidence Trap", the growing gap between geometric accuracy and probabilistic consistency. The data and code used to generate this figure are publicly available at https://github.com/Seba-san/SLAM-confidence-bibliometric