While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This paper presents SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment) that is a generic and explainable framework that propagates uncertainties via sensitivities for real-time risk assessment in VIO. The scientific novelty lies in the derivation of a real-time risk indicator that is backend-agnostic and exploits the Schur complement blocks of the Gauss-Newton normal matrix to propagate uncertainties. Practically, the Schur complement captures the sensitivity that reflects the influence of the uncertainty on the risk occurrence. Our framework estimates risks on the basis of the residual magnitudes, geometric conditioning, and short horizon temporal trends without requiring ground truth knowledge. Our framework enables to reliably predict trajectory degradation 50 frames ahead with an improvement of 20% to the baseline. In addition, SUPER initiates a stop or relocalization policy with 89.1% recall. The framework is backend agnostic and operates in real time with less than 0.2% additional CPU cost. Experiments show that SUPER provides consistent uncertainty estimates. A SLAM evaluation highlights the applicability to long horizon mapping.

翻译：尽管许多视觉里程计（VO）、视觉惯性里程计（VIO）以及SLAM系统能够实现高精度定位，但现有方法大多未能实现运行时的风险评估。本文提出SUPER（基于灵敏度的不确定性感知性能与风险评估框架），这是一个通用且可解释的框架，通过灵敏度传播不确定性以实现VIO的实时风险评估。其科学创新在于推导出一种与后端解耦的实时风险指标，该指标利用高斯-牛顿法正规矩阵的舒尔补块来传播不确定性。在实际应用中，舒尔补捕获的灵敏度反映了不确定性对风险发生的影响程度。本框架基于残差幅值、几何条件数及短时域时序趋势进行风险评估，无需依赖真实轨迹信息。实验表明，该框架能够可靠地预测未来50帧的轨迹退化，相比基线方法性能提升20%。此外，SUPER触发的停止或重定位策略召回率达到89.1%。该框架与后端实现无关，可实时运行，额外CPU开销低于0.2%。实验验证SUPER能提供一致的不确定性估计。一项SLAM评估进一步证明了其在长时程建图任务中的适用性。