  Robustness and resilience of simultaneous localization and mapping (SLAM) are
critical requirements for modern autonomous robotic systems. One of the
essential steps to achieve robustness and resilience is the ability of SLAM to
have an integrity measure for its localization estimates, and thus, have
internal fault tolerance mechanisms to deal with performance degradation. In
this work, we introduce a novel method for predicting SLAM localization error
based on the characterization of raw sensor inputs. The proposed method relies
on using a random forest regression model trained on 1-D global pooled features
that are generated from characterized raw sensor data. The model is validated
by using it to predict the performance of ORB-SLAM3 on three different datasets
running on four different operating modes, resulting in an average prediction
accuracy of up to 94.7\%. The paper also studies the impact of 12 different 1-D
global pooling functions on regression quality, and the superiority of 1-D
global averaging is quantitatively proven. Finally, the paper studies the
quality of prediction with limited training data, and proves that we are able
to maintain proper prediction quality when only 20 \% of the training examples
are used for training, which highlights how the proposed model can optimize the
evaluation footprint of SLAM systems.
