# Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

## TL;DR

This paper enhances Lidar-Monocular Visual Odometry (LIMO) by applying a genetic algorithm to optimize its parameters, significantly improving localization accuracy on the KITTI dataset.

## Contribution

It introduces a genetic algorithm-based parameter optimization method for LIMO, addressing manual tuning challenges and improving odometry performance.

## Key findings

- Genetic algorithm reduces translation error in LIMO.
- Optimized parameters improve localization accuracy.
- Method validated on KITTI dataset.

## Abstract

Lidar-Monocular Visual Odometry (LIMO), a odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking camera features as well as features from LIDAR measurements, and it estimates the motion using Bundle Adjustment based on robust key frames. For rejecting the outliers, LIMO uses semantic labelling and weights of the vegetation landmarks. A drawback of LIMO as well as many other odometry estimation algorithms is that it has many parameters that need to be manually adjusted according to the dynamic changes in the environment in order to decrease the translational errors. In this paper, we present and argue the use of Genetic Algorithm to optimize parameters with reference to LIMO and maximize LIMO's localization and motion estimation performance. We evaluate our approach on the well known KITTI odometry dataset and show that the genetic algorithm helps LIMO to reduce translation error in different datasets.