Map-Free Lidar Odometry (MFLO) Using a Range Flow Constraint and Point Patch Covariances

We present a lightweight real-time method to extract 3D ego-motion using a range flow constraint equation, point patch covariance, and a least squares solution. Our method exploits the structured data provided by range sensors, like rotating LiDARs, to attain 6 DOF odometry without building a map or scan-matching. To evaluate the performance of MFLO, a quadrotor was flown in various environments, and results indicate that MFLO matches and sometimes exceeds the performance of other LiDAR-based odometry methods while using fewer computational resources. In underground environments, MFLO captured 95.7% of the total vertical displacement for a 17.5 m translation upwards through a missile silo, the most of any other LiDAR algorithm evaluated in this study, and captured 92.8% of the total translation for a 42 m translation through an underground mine. In a motion capture lab, MFLO performed with only a 0.89–2.87% displacement percent error and 1.03–2.97% in final position error comparing to ground truth, making it the most consistent LiDAR odometry algorithm without mapping.