9,113 research outputs found
Dynamic Body VSLAM with Semantic Constraints
Image based reconstruction of urban environments is a challenging problem
that deals with optimization of large number of variables, and has several
sources of errors like the presence of dynamic objects. Since most large scale
approaches make the assumption of observing static scenes, dynamic objects are
relegated to the noise modeling section of such systems. This is an approach of
convenience since the RANSAC based framework used to compute most multiview
geometric quantities for static scenes naturally confine dynamic objects to the
class of outlier measurements. However, reconstructing dynamic objects along
with the static environment helps us get a complete picture of an urban
environment. Such understanding can then be used for important robotic tasks
like path planning for autonomous navigation, obstacle tracking and avoidance,
and other areas. In this paper, we propose a system for robust SLAM that works
in both static and dynamic environments. To overcome the challenge of dynamic
objects in the scene, we propose a new model to incorporate semantic
constraints into the reconstruction algorithm. While some of these constraints
are based on multi-layered dense CRFs trained over appearance as well as motion
cues, other proposed constraints can be expressed as additional terms in the
bundle adjustment optimization process that does iterative refinement of 3D
structure and camera / object motion trajectories. We show results on the
challenging KITTI urban dataset for accuracy of motion segmentation and
reconstruction of the trajectory and shape of moving objects relative to ground
truth. We are able to show average relative error reduction by a significant
amount for moving object trajectory reconstruction relative to state-of-the-art
methods like VISO 2, as well as standard bundle adjustment algorithms
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with depth measurements obtained from direct monocular SLAM. Our
fusion scheme privileges depth prediction in image locations where monocular
SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of
the reconstruction, hence overcoming one of the major limitations of monocular
SLAM. Finally, we propose a framework to efficiently fuse semantic labels,
obtained from a single frame, with dense SLAM, yielding semantically coherent
scene reconstruction from a single view. Evaluation results on two benchmark
datasets show the robustness and accuracy of our approach.Comment: 10 pages, 6 figures, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017. The first two
authors contribute equally to this pape
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