Enabling Aggressive Motion Estimation at Low-drift and Accurate Mapping in Real-time

Ji Zhang and Sanjiv Singh
Conference Paper, IEEE International Conference on Robotics and Automation (ICRA), May, 2017

Download Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


We present a data processing pipeline to online estimate ego-motion and build a map of the traversed environment, leveraging data from a 3D laser, a camera, and an IMU. Different from traditional methods that use a Kalman filter or factor-graph optimization, the proposed method employs a sequential, multi-layer processing pipeline, solving for motion from coarse to fine. The resulting system enables highfrequency, low-latency ego-motion estimation, along with dense, accurate 3D map registration. Further, the system is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in dark, textureless, and structure-less environments. During experiments, the system demonstrates 0.22% of relative position drift over 9.3km of navigation and robustness w.r.t aggressive motion such as highway speed driving (up to 33m/s).

author = {Ji Zhang and Sanjiv Singh},
title = {Enabling Aggressive Motion Estimation at Low-drift and Accurate Mapping in Real-time},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2017},
month = {May},
} 2019-01-18T11:01:49-04:00