Visual-LiDAR Odometry Aided by Reduced IMU

Yashar Balazadegan Sarvrood,Yang Gao,Siavash Hosseinyalamdary

doi:10.3390/ijgi5010003

Yashar Balazadegan Sarvrood, Yang Gao + Show 1 more

Open Access

https://doi.org/10.3390/ijgi5010003

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Abstract

This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art.

Highlights

Stand-alone stereo visual odometer, Light Detection And Ranging (LiDAR) odometer and Inertial Measurement Unit provide 6-DOF (Degree Of Freedom) state estimation
ICP algorithms, accumulate error over time, and it is prone to be erroneous under fast motion; it fails, if the point clouds are very sparse, especially in suburban areas where two sides of the road are covered with vegetation
A new integration method for stereo visual-LiDAR odometry and 3D (Three-Dimensional) reduced Inertial Measurement Unit (IMU) is proposed in this paper

Summary

Introduction

Stand-alone stereo visual odometer, LiDAR odometer and Inertial Measurement Unit provide 6-DOF (Degree Of Freedom) state estimation. ICP algorithms, accumulate error over time, and it is prone to be erroneous under fast motion; it fails, if the point clouds are very sparse, especially in suburban areas where two sides of the road are covered with vegetation. ICP algorithms always converge to the local minimum It needs a good initial guess of transformation to converge to the global minimum [3]. Another problem of ego motion estimation by moving the LiDAR odometer, involves motion distortion in point clouds due to the different receiving time of the range measurements [4]. Stereo VO (Visual Odometry) gives a good initial guess for ICP algorithm and helps compensating the motion distortion.

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Conclusion