11,407 research outputs found
Understanding and ameliorating non-linear phase and amplitude responses in AMCW Lidar
Amplitude modulated continuous wave (AMCW) lidar systems commonly suffer from non-linear phase and amplitude responses due to a number of known factors such as aliasing and multipath inteference. In order to produce useful range and intensity information it is necessary to remove these perturbations from the measurements. We review the known causes of non-linearity, namely aliasing, temporal variation in correlation waveform shape and mixed pixels/multipath inteference. We also introduce other sources of non-linearity, including crosstalk, modulation waveform envelope decay and non-circularly symmetric noise statistics, that have been ignored in the literature. An experimental study is conducted to evaluate techniques for mitigation of non-linearity, and it is found that harmonic cancellation provides a significant improvement in phase and amplitude linearity
A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry
The demand for autonomous vehicles is increasing gradually owing to their
enormous potential benefits. However, several challenges, such as vehicle
localization, are involved in the development of autonomous vehicles. A simple
and secure algorithm for vehicle positioning is proposed herein without
massively modifying the existing transportation infrastructure. For vehicle
localization, vehicles on the road are classified into two categories: host
vehicles (HVs) are the ones used to estimate other vehicles' positions and
forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV
transmits modulated data from the tail (or back) light, and the camera of the
HV receives that signal using optical camera communication (OCC). In addition,
the streetlight (SL) data are considered to ensure the position accuracy of the
HV. Determining the HV position minimizes the relative position variation
between the HV and FV. Using photogrammetry, the distance between FV or SL and
the camera of the HV is calculated by measuring the occupied image area on the
image sensor. Comparing the change in distance between HV and SLs with the
change in distance between HV and FV, the positions of FVs are determined. The
performance of the proposed technique is analyzed, and the results indicate a
significant improvement in performance. The experimental distance measurement
validated the feasibility of the proposed scheme
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
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