17,473 research outputs found
J-MOD: Joint Monocular Obstacle Detection and Depth Estimation
In this work, we propose an end-to-end deep architecture that jointly learns
to detect obstacles and estimate their depth for MAV flight applications. Most
of the existing approaches either rely on Visual SLAM systems or on depth
estimation models to build 3D maps and detect obstacles. However, for the task
of avoiding obstacles this level of complexity is not required. Recent works
have proposed multi task architectures to both perform scene understanding and
depth estimation. We follow their track and propose a specific architecture to
jointly estimate depth and obstacles, without the need to compute a global map,
but maintaining compatibility with a global SLAM system if needed. The network
architecture is devised to exploit the joint information of the obstacle
detection task, that produces more reliable bounding boxes, with the depth
estimation one, increasing the robustness of both to scenario changes. We call
this architecture J-MOD. We test the effectiveness of our approach with
experiments on sequences with different appearance and focal lengths and
compare it to SotA multi task methods that jointly perform semantic
segmentation and depth estimation. In addition, we show the integration in a
full system using a set of simulated navigation experiments where a MAV
explores an unknown scenario and plans safe trajectories by using our detection
model
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Estimating the 6D pose of known objects is important for robots to interact
with the real world. The problem is challenging due to the variety of objects
as well as the complexity of a scene caused by clutter and occlusions between
objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
for 6D object pose estimation. PoseCNN estimates the 3D translation of an
object by localizing its center in the image and predicting its distance from
the camera. The 3D rotation of the object is estimated by regressing to a
quaternion representation. We also introduce a novel loss function that enables
PoseCNN to handle symmetric objects. In addition, we contribute a large scale
video dataset for 6D object pose estimation named the YCB-Video dataset. Our
dataset provides accurate 6D poses of 21 objects from the YCB dataset observed
in 92 videos with 133,827 frames. We conduct extensive experiments on our
YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is
highly robust to occlusions, can handle symmetric objects, and provide accurate
pose estimation using only color images as input. When using depth data to
further refine the poses, our approach achieves state-of-the-art results on the
challenging OccludedLINEMOD dataset. Our code and dataset are available at
https://rse-lab.cs.washington.edu/projects/posecnn/.Comment: Accepted to RSS 201
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
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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