# Robust Photogeometric Localization over Time for Map-Centric Loop Closure

## TL;DR

This paper introduces a robust photogeometric localization method for map-centric SLAM that combines LiDAR and camera data, effectively validating loop closures and improving accuracy and robustness over traditional methods.

## Contribution

It presents a tightly coupled photogeometric localization approach that enhances loop closure validation and outlier rejection in map-centric SLAM.

## Key findings

- More accurate than conventional ICP methods
- Robust to incorrect initial pose guesses
- Effective outlier rejection using visual evidence

## Abstract

Map-centric SLAM is emerging as an alternative of conventional graph-based SLAM for its accuracy and efficiency in long-term mapping problems. However, in map-centric SLAM, the process of loop closure differs from that of conventional SLAM and the result of incorrect loop closure is more destructive and is not reversible. In this paper, we present a tightly coupled photogeometric metric localization for the loop closure problem in map-centric SLAM. In particular, our method combines complementary constraints from LiDAR and camera sensors, and validates loop closure candidates with sequential observations. The proposed method provides a visual evidence-based outlier rejection where failures caused by either place recognition or localization outliers can be effectively removed. We demonstrate the proposed method is not only more accurate than the conventional global ICP methods but is also robust to incorrect initial pose guesses.