3,775 research outputs found
Estimation of skeletal kinematics in freely moving rodents
Forming a complete picture of the relationship between neural activity and skeletal kinematics requires quantification of skeletal joint biomechanics during free behavior; however, without detailed knowledge of the underlying skeletal motion, inferring limb kinematics using surface-tracking approaches is difficult, especially for animals where the relationship between the surface and underlying skeleton changes during motion. Here we developed a videography-based method enabling detailed three-dimensional kinematic quantification of an anatomically defined skeleton in untethered freely behaving rats and mice. This skeleton-based model was constrained using anatomical principles and joint motion limits and provided skeletal pose estimates for a range of body sizes, even when limbs were occluded. Model-inferred limb positions and joint kinematics during gait and gap-crossing behaviors were verified by direct measurement of either limb placement or limb kinematics using inertial measurement units. Together we show that complex decision-making behaviors can be accurately reconstructed at the level of skeletal kinematics using our anatomically constrained model
Scalable Estimation of Precision Maps in a MapReduce Framework
This paper presents a large-scale strip adjustment method for LiDAR mobile
mapping data, yielding highly precise maps. It uses several concepts to achieve
scalability. First, an efficient graph-based pre-segmentation is used, which
directly operates on LiDAR scan strip data, rather than on point clouds.
Second, observation equations are obtained from a dense matching, which is
formulated in terms of an estimation of a latent map. As a result of this
formulation, the number of observation equations is not quadratic, but rather
linear in the number of scan strips. Third, the dynamic Bayes network, which
results from all observation and condition equations, is partitioned into two
sub-networks. Consequently, the estimation matrices for all position and
orientation corrections are linear instead of quadratic in the number of
unknowns and can be solved very efficiently using an alternating least squares
approach. It is shown how this approach can be mapped to a standard key/value
MapReduce implementation, where each of the processing nodes operates
independently on small chunks of data, leading to essentially linear
scalability. Results are demonstrated for a dataset of one billion measured
LiDAR points and 278,000 unknowns, leading to maps with a precision of a few
millimeters.Comment: ACM SIGSPATIAL'16, October 31-November 03, 2016, Burlingame, CA, US
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