arXiv:2602.15884cs.RO2026-02
SLAM系统过度追求精度分数，忽视不确定性估计，导致结果不可靠。
The SLAM Confidence Trap
- 提出将不确定性实时计算作为核心评价指标
- 现有系统虽几何准确但概率不一致，易出错
- 适合关注系统可靠性的机器人与自动驾驶研究者
SLAM领域陷入'信心陷阱'，即过分重视基准测试得分，而忽略严谨的不确定性估计。这导致系统虽在几何上精确，但在概率上不一致且脆弱。本文倡导范式转变：将一致且实时的不确定性计算作为成功的核心度量标准。
原文摘要 · Abstract (English)
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.
SLAM不确定性机器人
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