This thesis investigates some of the sensing and perception challenges faced
by multi-robot teams equipped with LIDAR and camera
sensors. Multi-robot teams are ideal for deployment in large,
real-world environments due to their ability to parallelize exploration,
reconnaissance or mapping tasks.
However, such domains also impose additional requirements, including the
need for a) online algorithms (to eliminate stopping and waiting for
processing to finish before proceeding) and b) scalability (to handle
data from many robots distributed over a large area).
These general requirements give rise to specific algorithmic challenges, including 1) online maintenance of large, coherent
maps covering the explored area, 2) online estimation of communication properties
in the presence of buildings and other interfering structure, and 3)
online fusion and segmentation of multiple sensors to aid in object detection.
The contribution of this thesis is the introduction of novel
approaches that leverage grid-maps and sparse multi-variate gaussian
inference to augment the capability of multi-robot teams operating in
urban, indoor-outdoor environments by improving the state of the art
of map rasterization, signal strength prediction, colored point cloud
segmentation, and reliable camera calibration.
In particular, we introduce a map rasterization technique for large
LIDAR-based occupancy grids that makes online updates possible when
data is arriving from many robots at once. We also introduce new
online techniques for robots to predict the signal strength to their
teammates by combining LIDAR measurements with signal strength
measurements from their radios. Processing fused LIDAR+camera point
clouds is also important for many object-detection pipelines. We
demonstrate a near linear-time online segmentation algorithm to this
domain. However, maintaining the calibration of a fleet of 14 robots
made this approach difficult to employ in practice.
Therefore we introduced a robust and repeatable
camera calibration process that grounds the camera model uncertainty in pixel
error, allowing the system to guide novices and experts alike to reliably produce accurate calibrations.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113516/1/jhstrom_1.pd