# Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization

## TL;DR

This paper presents a novel ensemble learning regression approach using 1-D global pooling of raw sensor data to accurately predict SLAM localization errors, enhancing robustness and fault tolerance in autonomous systems.

## Contribution

It introduces a new method combining random forest regression with 1-D global pooled features for SLAM error prediction, validated across multiple datasets and operating modes.

## Key findings

- Achieved up to 94.7% prediction accuracy.
- Identified 1-D global averaging as the most effective pooling function.
- Maintained prediction quality with only 20% training data.

## Abstract

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.