15,663 research outputs found
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
Recurrent Scene Parsing with Perspective Understanding in the Loop
Objects may appear at arbitrary scales in perspective images of a scene,
posing a challenge for recognition systems that process images at a fixed
resolution. We propose a depth-aware gating module that adaptively selects the
pooling field size in a convolutional network architecture according to the
object scale (inversely proportional to the depth) so that small details are
preserved for distant objects while larger receptive fields are used for those
nearby. The depth gating signal is provided by stereo disparity or estimated
directly from monocular input. We integrate this depth-aware gating into a
recurrent convolutional neural network to perform semantic segmentation. Our
recurrent module iteratively refines the segmentation results, leveraging the
depth and semantic predictions from the previous iterations.
Through extensive experiments on four popular large-scale RGB-D datasets, we
demonstrate this approach achieves competitive semantic segmentation
performance with a model which is substantially more compact. We carry out
extensive analysis of this architecture including variants that operate on
monocular RGB but use depth as side-information during training, unsupervised
gating as a generic attentional mechanism, and multi-resolution gating. We find
that gated pooling for joint semantic segmentation and depth yields
state-of-the-art results for quantitative monocular depth estimation
Semantic 3D Occupancy Mapping through Efficient High Order CRFs
Semantic 3D mapping can be used for many applications such as robot
navigation and virtual interaction. In recent years, there has been great
progress in semantic segmentation and geometric 3D mapping. However, it is
still challenging to combine these two tasks for accurate and large-scale
semantic mapping from images. In the paper, we propose an incremental and
(near) real-time semantic mapping system. A 3D scrolling occupancy grid map is
built to represent the world, which is memory and computationally efficient and
bounded for large scale environments. We utilize the CNN segmentation as prior
prediction and further optimize 3D grid labels through a novel CRF model.
Superpixels are utilized to enforce smoothness and form robust P N high order
potential. An efficient mean field inference is developed for the graph
optimization. We evaluate our system on the KITTI dataset and improve the
segmentation accuracy by 10% over existing systems.Comment: IROS 201
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
3D scene understanding is important for robots to interact with the 3D world
in a meaningful way. Most previous works on 3D scene understanding focus on
recognizing geometrical or semantic properties of the scene independently. In
this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a
novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a
new recurrent neural network architecture for semantic labeling on RGB-D
videos. The output of the network is integrated with mapping techniques such as
KinectFusion in order to inject semantic information into the reconstructed 3D
scene. Experiments conducted on a real world dataset and a synthetic dataset
with RGB-D videos demonstrate the ability of our method in semantic 3D scene
mapping.Comment: Published in RSS 201
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