49,296 research outputs found
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
Semantic 3D mapping can be used for many applications such as robot
navigation and virtual interaction. In recent years, there has been great
progress in semantic segmentation and geometric 3D mapping. However, it is
still challenging to combine these two tasks for accurate and large-scale
semantic mapping from images. In the paper, we propose an incremental and
(near) real-time semantic mapping system. A 3D scrolling occupancy grid map is
built to represent the world, which is memory and computationally efficient and
bounded for large scale environments. We utilize the CNN segmentation as prior
prediction and further optimize 3D grid labels through a novel CRF model.
Superpixels are utilized to enforce smoothness and form robust P N high order
potential. An efficient mean field inference is developed for the graph
optimization. We evaluate our system on the KITTI dataset and improve the
segmentation accuracy by 10% over existing systems.Comment: IROS 201
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Many algorithms in computer vision and robotics make strong assumptions about
uncertainty, and rely on the validity of these assumptions to produce accurate
and consistent state estimates. In practice, dynamic environments may degrade
sensor performance in predictable ways that cannot be captured with static
uncertainty parameters. In this paper, we employ fast nonparametric Bayesian
inference techniques to more accurately model sensor uncertainty. By setting a
prior on observation uncertainty, we derive a predictive robust estimator, and
show how our model can be learned from sample images, both with and without
knowledge of the motion used to generate the data. We validate our approach
through Monte Carlo simulations, and report significant improvements in
localization accuracy relative to a fixed noise model in several settings,
including on synthetic data, the KITTI dataset, and our own experimental
platform.Comment: In Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'16), Stockholm, Sweden, May 16-21, 201
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