3,111 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
Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality
We address the problem of interactively controlling the workspace of a mobile
robot to ensure a human-aware navigation. This is especially of relevance for
non-expert users living in human-robot shared spaces, e.g. home environments,
since they want to keep the control of their mobile robots, such as vacuum
cleaning or companion robots. Therefore, we introduce virtual borders that are
respected by a robot while performing its tasks. For this purpose, we employ a
RGB-D Google Tango tablet as human-robot interface in combination with an
augmented reality application to flexibly define virtual borders. We evaluated
our system with 15 non-expert users concerning accuracy, teaching time and
correctness and compared the results with other baseline methods based on
visual markers and a laser pointer. The experimental results show that our
method features an equally high accuracy while reducing the teaching time
significantly compared to the baseline methods. This holds for different border
lengths, shapes and variations in the teaching process. Finally, we
demonstrated the correctness of the approach, i.e. the mobile robot changes its
navigational behavior according to the user-defined virtual borders.Comment: Accepted on 2018 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), supplementary video: https://youtu.be/oQO8sQ0JBR
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