CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR

TL;DR

This paper presents a CNN-based odometry estimation method using 3D LiDAR data encoded into 2D matrices, achieving real-time performance and improved accuracy over existing methods, especially when combined with IMU data.

Contribution

The paper introduces a novel CNN approach for LiDAR-based odometry that outperforms state-of-the-art methods like LOAM and supports rotational motion prediction.

Findings

Better translational motion estimation accuracy than LOAM

Real-time odometry estimation with high precision

Effective integration of IMU data for improved results

Abstract

We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time.