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

arXiv: https://arxiv.org/abs/1903.02046 · HTML: https://mlsanity.com/paper/1903.02046/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Registration of 3D Point Sets Using Correntropy Similarity Matrix](https://mlsanity.com/paper/2107.09725/) (2021)
- [Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms](https://mlsanity.com/paper/2207.11244/) (2022)
- [AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay](https://mlsanity.com/paper/2210.12892/) (2022)
- [Dense Visual Odometry Using Genetic Algorithm](https://mlsanity.com/paper/2311.06149/) (2023)
- [Registration of 3D Point Sets Using Exponential-based Similarity Matrix](https://mlsanity.com/paper/2505.04540/) (2025)

## Abstract (arXiv)

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.