3 research outputs found
Doppler-only Single-scan 3D Vehicle Odometry
We present a novel 3D odometry method that recovers the full motion of a
vehicle only from a Doppler-capable range sensor. It leverages the radial
velocities measured from the scene, estimating the sensor's velocity from a
single scan. The vehicle's 3D motion, defined by its linear and angular
velocities, is calculated taking into consideration its kinematic model which
provides a constraint between the velocity measured at the sensor frame and the
vehicle frame.
Experiments carried out prove the viability of our single-sensor method
compared to mounting an additional IMU. Our method provides the translation of
the sensor, which cannot be reliably determined from an IMU, as well as its
rotation. Its short-term accuracy and fast operation (~5ms) make it a proper
candidate to supply the initialization to more complex localization algorithms
or mapping pipelines. Not only does it reduce the error of the mapper, but it
does so at a comparable level of accuracy as an IMU would. All without the need
to mount and calibrate an extra sensor on the vehicle.Comment: This work has been submitted to the IEEE for possible publication.
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Efficient 3D Lidar Odometry Based on Planar Patches
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
Doppler-only Single-scan 3D Vehicle Odometry
Dataset provided with the article of the same name. Created to test the performance of 3D Doppler-capable radar odometry in outdoor scenarios. Sensors mounted on the vehicle include a 3D Doppler-capable radar, 3D lidar, and an IMU