3,798 research outputs found
Anomaly detection & object classification using multi-spectral LiDAR and sonar
In this thesis, we present the theory of high-dimensional signal approximation of multifrequency signals. We also present both linear and non-linear compressive sensing (CS)
algorithms that generate encoded representations of time-correlated single photon counting (TCSPC) light detection and ranging (LiDAR) data, side-scan sonar (SSS) and synthetic aperture sonar (SAS). The main contributions of this thesis are summarised as
follows:
1. Research is carried out studying full-waveform (FW) LiDARs, in particular, the
TCSPC data, capture, storage and processing.
2. FW-LiDARs are capable of capturing large quantities of photon-counting data in
real-time. However, the real-time processing of the raw LiDAR waveforms hasn’t
been widely exploited. This thesis answers some of the fundamental questions:
• can semantic information be extracted and encoded from raw multi-spectral
FW-LiDAR signals?
• can these encoded representations then be used for object segmentation and
classification?
3. Research is carried out into signal approximation and compressive sensing techniques, its limitations and the application domains.
4. Research is also carried out in 3D point cloud processing, combining geometric features with material spectra (spectral-depth representation), for object segmentation
and classification.
5. Extensive experiments have been carried out with publicly available datasets, e.g.
the Washington RGB Image and Depth (RGB-D) dataset [108], YaleB face dataset1
[110], real-world multi-frequency aerial laser scans (ALS)2 and an underwater multifrequency (16 wavelengths) TCSPC dataset collected using custom-build targets
especially for this thesis.
6. The multi-spectral measurements were made underwater on targets with different shapes and materials. A novel spectral-depth representation is presented with
strong discrimination characteristics on target signatures. Several custom-made
and realistically scaled exemplars with known and unknown targets have been investigated using a multi-spectral single photon counting LiDAR system.
7. In this work, we also present a new approach to peak modelling and classification
for waveform enabled LiDAR systems. Not all existing approaches perform peak
modelling and classification simultaneously in real-time. This was tested on both
simulated waveform enabled LiDAR data and real ALS data2
.
This PhD also led to an industrial secondment at Carbomap, Edinburgh, where some of
the waveform modelling algorithms were implemented in C++ and CUDA for Nvidia TX1
boards for real-time performance.
1http://vision.ucsd.edu/~leekc/ExtYaleDatabase/
2This dataset was captured in collaboration with Carbomap Ltd. Edinburgh, UK. The data was
collected during one of the trials in Austria using commercial-off-the-shelf (COTS) sensors
Multi-View 3D Object Detection Network for Autonomous Driving
This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view representation. The network is composed of two subnetworks: one for
3D object proposal generation and another for multi-view feature fusion. The
proposal network generates 3D candidate boxes efficiently from the bird's eye
view representation of 3D point cloud. We design a deep fusion scheme to
combine region-wise features from multiple views and enable interactions
between intermediate layers of different paths. Experiments on the challenging
KITTI benchmark show that our approach outperforms the state-of-the-art by
around 25% and 30% AP on the tasks of 3D localization and 3D detection. In
addition, for 2D detection, our approach obtains 10.3% higher AP than the
state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data
This paper presents a novel method for ground segmentation in Velodyne point
clouds. We propose an encoding of sparse 3D data from the Velodyne sensor
suitable for training a convolutional neural network (CNN). This general
purpose approach is used for segmentation of the sparse point cloud into ground
and non-ground points. The LiDAR data are represented as a multi-channel 2D
signal where the horizontal axis corresponds to the rotation angle and the
vertical axis the indexes channels (i.e. laser beams). Multiple topologies of
relatively shallow CNNs (i.e. 3-5 convolutional layers) are trained and
evaluated using a manually annotated dataset we prepared. The results show
significant improvement of performance over the state-of-the-art method by
Zhang et al. in terms of speed and also minor improvements in terms of
accuracy.Comment: ICRA 2018 submissio
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
To assure that an autonomous car is driving safely on public roads, its
object detection module should not only work correctly, but show its prediction
confidence as well. Previous object detectors driven by deep learning do not
explicitly model uncertainties in the neural network. We tackle with this
problem by presenting practical methods to capture uncertainties in a 3D
vehicle detector for Lidar point clouds. The proposed probabilistic detector
represents reliable epistemic uncertainty and aleatoric uncertainty in
classification and localization tasks. Experimental results show that the
epistemic uncertainty is related to the detection accuracy, whereas the
aleatoric uncertainty is influenced by vehicle distance and occlusion. The
results also show that we can improve the detection performance by 1%-5% by
modeling the aleatoric uncertainty.Comment: Accepted to present in the 21st IEEE International Conference on
Intelligent Transportation Systems (ITSC 2018
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