48,157 research outputs found
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
In this work we present a novel framework that uses deep learning to predict
object feature points that are out-of-view in the input image. This system was
developed with the application of model-based tracking in mind, particularly in
the case of autonomous inspection robots, where only partial views of the
object are available. Out-of-view prediction is enabled by applying scaling to
the feature point labels during network training. This is combined with a
recurrent neural network architecture designed to provide the final prediction
layers with rich feature information from across the spatial extent of the
input image. To show the versatility of these out-of-view predictions, we
describe how to integrate them in both a particle filter tracker and an
optimisation based tracker. To evaluate our work we compared our framework with
one that predicts only points inside the image. We show that as the amount of
the object in view decreases, being able to predict outside the image bounds
adds robustness to the final pose estimation.Comment: Submitted to IROS 201
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
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Recent robotic manipulation competitions have highlighted that sophisticated
robots still struggle to achieve fast and reliable perception of task-relevant
objects in complex, realistic scenarios. To improve these systems' perceptive
speed and robustness, we present SegICP, a novel integrated solution to object
recognition and pose estimation. SegICP couples convolutional neural networks
and multi-hypothesis point cloud registration to achieve both robust pixel-wise
semantic segmentation as well as accurate and real-time 6-DOF pose estimation
for relevant objects. Our architecture achieves 1cm position error and
<5^\circ$ angle error in real time without an initial seed. We evaluate and
benchmark SegICP against an annotated dataset generated by motion capture.Comment: IROS camera-read
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking problem is tackled by identifying a subset of these line
segments and connecting them sequentially to form a closed boundary with the
largest saliency and a certain similarity to the previous one. Specifically, we
define a new tracking criterion which combines a grouping cost and an area
similarity constraint. The proposed criterion makes the resulting boundary
tracking more robust to local minima. To achieve real-time tracking
performance, we use Delaunay Triangulation to build a graph model with the
detected line segments and then reduce the tracking problem to finding the
optimal cycle in this graph. This is solved by our newly proposed closed
boundary candidates searching algorithm called "Bidirectional Shortest Path
(BDSP)". The efficiency and robustness of the proposed method are tested on
real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017) submission ID 103
Probabilistic Global Scale Estimation for MonoSLAM Based on Generic Object Detection
This paper proposes a novel method to estimate the global scale of a 3D
reconstructed model within a Kalman filtering-based monocular SLAM algorithm.
Our Bayesian framework integrates height priors over the detected objects
belonging to a set of broad predefined classes, based on recent advances in
fast generic object detection. Each observation is produced on single frames,
so that we do not need a data association process along video frames. This is
because we associate the height priors with the image region sizes at image
places where map features projections fall within the object detection regions.
We present very promising results of this approach obtained on several
experiments with different object classes.Comment: Int. Workshop on Visual Odometry, CVPR, (July 2017
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