Kavli Affiliate: Yi Zhou
| First 5 Authors: Junkai Niu, Sheng Zhong, Xiuyuan Lu, Shaojie Shen, Guillermo Gallego
| Summary:
Event-based visual odometry is a specific branch of visual Simultaneous
Localization and Mapping (SLAM) techniques, which aims at solving tracking and
mapping subproblems (typically in parallel), by exploiting the special working
principles of neuromorphic (i.e., event-based) cameras. Due to the
motion-dependent nature of event data, explicit data association (i.e., feature
matching) under large-baseline view-point changes is difficult to establish,
making direct methods a more rational choice. However, state-of-the-art direct
methods are limited by the high computational complexity of the mapping
sub-problem and the degeneracy of camera pose tracking in certain degrees of
freedom (DoF) in rotation. In this paper, we tackle these issues by building an
event-based stereo visual-inertial odometry system on top of a direct pipeline.
Specifically, to speed up the mapping operation, we propose an efficient
strategy for sampling contour points according to the local dynamics of events.
The mapping performance is also improved in terms of structure completeness and
local smoothness by merging the temporal stereo and static stereo results. To
circumvent the degeneracy of camera pose tracking in recovering the pitch and
yaw components of general 6-DoF motion, we introduce IMU measurements as motion
priors via pre-integration. To this end, a compact back-end is proposed for
continuously updating the IMU bias and predicting the linear velocity, enabling
an accurate motion prediction for camera pose tracking. The resulting system
scales well with modern high-resolution event cameras and leads to better
global positioning accuracy in large-scale outdoor environments. Extensive
evaluations on five publicly available datasets featuring different resolutions
and scenarios justify the superior performance of the proposed system against
five state-of-the-art methods.
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