193 research outputs found
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion
This paper revisits the problem of continuous-time structure from motion, and
introduces a number of extensions that improve convergence and efficiency. The
formulation with a -continuous spline for the trajectory
naturally incorporates inertial measurements, as derivatives of the sought
trajectory. We analyse the behaviour of split interpolation on
and on , and a joint interpolation on , and show
that the latter implicitly couples the direction of translation and rotation.
Such an assumption can make good sense for a camera mounted on a robot arm, but
not for hand-held or body-mounted cameras. Our experiments show that split
interpolation on and on is preferable over
interpolation in all tested cases. Finally, we investigate the
problem of landmark reprojection on rolling shutter cameras, and show that the
tested reprojection methods give similar quality, while their computational
load varies by a factor of 2.Comment: Submitted to IJR
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry
We present a direct visual-inertial odometry (VIO) method which estimates the
motion of the sensor setup and sparse 3D geometry of the environment based on
measurements from a rolling-shutter camera and an inertial measurement unit
(IMU).
The visual part of the system performs a photometric bundle adjustment on a
sparse set of points. This direct approach does not extract feature points and
is able to track not only corners, but any pixels with sufficient gradient
magnitude. Neglecting rolling-shutter effects in the visual part severely
degrades accuracy and robustness of the system. In this paper, we incorporate a
rolling-shutter model into the photometric bundle adjustment that estimates a
set of recent keyframe poses and the inverse depth of a sparse set of points.
IMU information is accumulated between several frames using measurement
preintegration, and is inserted into the optimization as an additional
constraint between selected keyframes. For every keyframe we estimate not only
the pose but also velocity and biases to correct the IMU measurements. Unlike
systems with global-shutter cameras, we use both IMU measurements and
rolling-shutter effects of the camera to estimate velocity and biases for every
state.
Last, we evaluate our system on a novel dataset that contains global-shutter
and rolling-shutter images, IMU data and ground-truth poses for ten different
sequences, which we make publicly available. Evaluation shows that the proposed
method outperforms a system where rolling shutter is not modelled and achieves
similar accuracy to the global-shutter method on global-shutter data
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