  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.
