2,936 research outputs found
Learning a Bias Correction for Lidar-only Motion Estimation
This paper presents a novel technique to correct for bias in a classical
estimator using a learning approach. We apply a learned bias correction to a
lidar-only motion estimation pipeline. Our technique trains a Gaussian process
(GP) regression model using data with ground truth. The inputs to the model are
high-level features derived from the geometry of the point-clouds, and the
outputs are the predicted biases between poses computed by the estimator and
the ground truth. The predicted biases are applied as a correction to the poses
computed by the estimator.
Our technique is evaluated on over 50km of lidar data, which includes the
KITTI odometry benchmark and lidar datasets collected around the University of
Toronto campus. After applying the learned bias correction, we obtained
significant improvements to lidar odometry in all datasets tested. We achieved
around 10% reduction in errors on all datasets from an already accurate lidar
odometry algorithm, at the expense of only less than 1% increase in
computational cost at run-time.Comment: 15th Conference on Computer and Robot Vision (CRV 2018
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
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