73 research outputs found
Probabilistic Perception System for Object Classification Based on Camera -LiDAR Sensor Fusion
International audienceOne of the most basic needs to guide the definition of urban, agro-industrial and territorial management policies is to have a digital topographic representation or map of cities, crops and forests. These maps should ideally be created from multiple sensors whose responses are complementary (color information, for example, complements the returns of a LiDAR sensor in the presence of rain or low reflective objects). Once a topographic representation has been constructed, it can be used to produce and geo-localize higher-level estimates (e.g., location and classification of different trees and plants, crop density, location, and types of pests). Data can be collected using terrestrial unmanned vehicles equipped with hyper-spectral cameras, stereo cameras and LiDAR (Light Detection and Ranging) sensors. The processing of the acquired data can be used to generate a digital forest model (DFM). DFM will support forest planners in making multi-criteria decisions (MCDA) when planning harvesting operations. However creating a DFM or the map of a city, require a highly accurate and dense point cloud of the environment at hand. Motivated for building 3D reconstructions from which representations of different vegetation features of an environment can be obtained with high quality and precision. A robust perception system is proposed for densely predicting depth, since it is an essential component in understanding the 3D geometry of a scene. It is known that cameras provide near instantaneous capture of the workspace’s appearance such as texture and color, but from a single view, little geometrical information. On the other hand, laser readings may be so sparse that significant information about the surface is missing. The considerations above motivate the formulation of this work’s research question: How to develop a perception system for fusing a laser scan with a RGB image in order to produce a higher-resolution range
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
With the advent of affordable depth sensors, 3D capture becomes more and more
ubiquitous and already has made its way into commercial products. Yet,
capturing the geometry or complete shapes of everyday objects using scanning
devices (e.g. Kinect) still comes with several challenges that result in noise
or even incomplete shapes. Recent success in deep learning has shown how to
learn complex shape distributions in a data-driven way from large scale 3D CAD
Model collections and to utilize them for 3D processing on volumetric
representations and thereby circumventing problems of topology and
tessellation. Prior work has shown encouraging results on problems ranging from
shape completion to recognition. We provide an analysis of such approaches and
discover that training as well as the resulting representation are strongly and
unnecessarily tied to the notion of object labels. Thus, we propose a full
convolutional volumetric auto encoder that learns volumetric representation
from noisy data by estimating the voxel occupancy grids. The proposed method
outperforms prior work on challenging tasks like denoising and shape
completion. We also show that the obtained deep embedding gives competitive
performance when used for classification and promising results for shape
interpolation
Recognizing Objects In-the-wild: Where Do We Stand?
The ability to recognize objects is an essential skill for a robotic system
acting in human-populated environments. Despite decades of effort from the
robotic and vision research communities, robots are still missing good visual
perceptual systems, preventing the use of autonomous agents for real-world
applications. The progress is slowed down by the lack of a testbed able to
accurately represent the world perceived by the robot in-the-wild. In order to
fill this gap, we introduce a large-scale, multi-view object dataset collected
with an RGB-D camera mounted on a mobile robot. The dataset embeds the
challenges faced by a robot in a real-life application and provides a useful
tool for validating object recognition algorithms. Besides describing the
characteristics of the dataset, the paper evaluates the performance of a
collection of well-established deep convolutional networks on the new dataset
and analyzes the transferability of deep representations from Web images to
robotic data. Despite the promising results obtained with such representations,
the experiments demonstrate that object classification with real-life robotic
data is far from being solved. Finally, we provide a comparative study to
analyze and highlight the open challenges in robot vision, explaining the
discrepancies in the performance
DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data
We introduce the DROW detector, a deep learning based detector for 2D range
data. Laser scanners are lighting invariant, provide accurate range data, and
typically cover a large field of view, making them interesting sensors for
robotics applications. So far, research on detection in laser range data has
been dominated by hand-crafted features and boosted classifiers, potentially
losing performance due to suboptimal design choices. We propose a Convolutional
Neural Network (CNN) based detector for this task. We show how to effectively
apply CNNs for detection in 2D range data, and propose a depth preprocessing
step and voting scheme that significantly improve CNN performance. We
demonstrate our approach on wheelchairs and walkers, obtaining state of the art
detection results. Apart from the training data, none of our design choices
limits the detector to these two classes, though. We provide a ROS node for our
detector and release our dataset containing 464k laser scans, out of which 24k
were annotated.Comment: Lucas Beyer and Alexander Hermans contributed equall
Monocular SLAM Supported Object Recognition
In this work, we develop a monocular SLAM-aware object recognition system
that is able to achieve considerably stronger recognition performance, as
compared to classical object recognition systems that function on a
frame-by-frame basis. By incorporating several key ideas including multi-view
object proposals and efficient feature encoding methods, our proposed system is
able to detect and robustly recognize objects in its environment using a single
RGB camera in near-constant time. Through experiments, we illustrate the
utility of using such a system to effectively detect and recognize objects,
incorporating multiple object viewpoint detections into a unified prediction
hypothesis. The performance of the proposed recognition system is evaluated on
the UW RGB-D Dataset, showing strong recognition performance and scalable
run-time performance compared to current state-of-the-art recognition systems.Comment: Accepted to appear at Robotics: Science and Systems 2015, Rome, Ital
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