11,410 research outputs found
3D Bounding Box Estimation Using Deep Learning and Geometry
We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geometric constraints provided by a 2D object bounding box to produce a
complete 3D bounding box. The first network output estimates the 3D object
orientation using a novel hybrid discrete-continuous loss, which significantly
outperforms the L2 loss. The second output regresses the 3D object dimensions,
which have relatively little variance compared to alternatives and can often be
predicted for many object types. These estimates, combined with the geometric
constraints on translation imposed by the 2D bounding box, enable us to recover
a stable and accurate 3D object pose. We evaluate our method on the challenging
KITTI object detection benchmark both on the official metric of 3D orientation
estimation and also on the accuracy of the obtained 3D bounding boxes. Although
conceptually simple, our method outperforms more complex and computationally
expensive approaches that leverage semantic segmentation, instance level
segmentation and flat ground priors and sub-category detection. Our
discrete-continuous loss also produces state of the art results for 3D
viewpoint estimation on the Pascal 3D+ dataset.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
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