Improving Ego-Velocity Estimation of Low-Cost Doppler Radars for Vehicles

Kingery, Aaron; Song, Dezhen

doi:10.1109/lra.2022.3191247

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…The radial velocity provided by these sensors has proven to be advantageous for odometry methods, aiding in the segmentation of dynamic and static objects [5], as well as introducing more constraints to the movement estimation [6]. Given the right conditions, it can be as powerful as to recover the 2D ego-motion of the sensor from a single scan of its surroundings [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Efficient 3D Lidar Odometry Based on Planar Patches

Galeote-Luque

Ruiz-Sarmiento

González-Jiménez

2022

Sensors

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In this paper we present a new way to compute the odometry of a 3D lidar in real-time. Due to the significant relation between these sensors and the rapidly increasing sector of autonomous vehicles, 3D lidars have improved in recent years, with modern models producing data in the form of range images. We take advantage of this ordered format to efficiently estimate the trajectory of the sensor as it moves in 3D space. The proposed method creates and leverages a flatness image in order to exploit the information found in flat surfaces of the scene. This allows for an efficient selection of planar patches from a first range image. Then, from a second image, keypoints related to said patches are extracted. This way, our proposal computes the ego-motion by imposing a coplanarity constraint between pairs <point, plane> whose correspondences are iteratively updated. The proposed algorithm is tested and compared with state-of-the-art ICP algorithms. Experiments show that our proposal, running on a single thread, can run 5× faster than a multi-threaded implementation of GICP, while providing a more accurate localization. A second version of the algorithm is also presented, which reduces the drift even further while needing less than half of the computation time of GICP. Both configurations of the algorithm run at frame rates common for most 3D lidars, 10 and 20 Hz on a standard CPU.

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