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Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

2019; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-030-33723-0_29

1611-3349

Adarsh Sehgal, Ashutosh Singandhupe, Hung Manh La, Alireza Tavakkoli, Sushil J. Louis,

Remote Sensing and LiDAR Applications

Lidar-Monocular Visual Odometry (LIMO), an odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking features from camera images and LIDAR measurements. LIMO then estimates the motion using Bundle Adjustment based on robust key frames. LIMO uses semantic labelling and weights of the vegetation landmarks for rejecting outliers. 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 dynamic changes in the environment in order to decrease translational errors. In this paper, we present and argue the use of Genetic Algorithm (GA) 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 GA helps LIMO to reduce translation error in different datasets.