47,068 research outputs found
DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments
Simultaneous Localization and Mapping (SLAM) is considered to be a
fundamental capability for intelligent mobile robots. Over the past decades,
many impressed SLAM systems have been developed and achieved good performance
under certain circumstances. However, some problems are still not well solved,
for example, how to tackle the moving objects in the dynamic environments, how
to make the robots truly understand the surroundings and accomplish advanced
tasks. In this paper, a robust semantic visual SLAM towards dynamic
environments named DS-SLAM is proposed. Five threads run in parallel in
DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and
dense semantic map creation. DS-SLAM combines semantic segmentation network
with moving consistency check method to reduce the impact of dynamic objects,
and thus the localization accuracy is highly improved in dynamic environments.
Meanwhile, a dense semantic octo-tree map is produced, which could be employed
for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in
the real-world environment. The results demonstrate the absolute trajectory
accuracy in DS-SLAM can be improved by one order of magnitude compared with
ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic
environments. Now the code is available at our github:
https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2018). Now the code is available at our
github: https://github.com/ivipsourcecode/DS-SLA
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.Comment: CVPR 201
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