55,074 research outputs found
High-Performance and Tunable Stereo Reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction
quality and have largely avoided prioritizing for run time performance. Robots,
on the other hand, require quick maneuverability and effective computation to
observe its immediate environment and perform tasks within it. In this work, we
propose a high-performance and tunable stereo disparity estimation method, with
a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can
potentially enable robots to quickly reconstruct their immediate surroundings
and maneuver at high-speeds. Our key contribution is a disparity estimation
algorithm that iteratively approximates the scene depth via a piece-wise planar
mesh from stereo imagery, with a fast depth validation step for semi-dense
reconstruction. The mesh is initially seeded with sparsely matched keypoints,
and is recursively tessellated and refined as needed (via a resampling stage),
to provide the desired stereo disparity accuracy. The inherent simplicity and
speed of our approach, with the ability to tune it to a desired reconstruction
quality and runtime performance makes it a compelling solution for applications
in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 2016; 8 pages, 5 figure
A Correlation-Based Fingerprint Verification System
In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates in the primary fingerprint, uses template matching to locate them in the secondary print, and compares the template positions of both fingerprints. Unlike minutiae-based systems, the correlation-based fingerprint verification system is capable of dealing with bad-quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions. Experiments have shown that the performance of this system at the moment is comparable to the performance of many other fingerprint verification systems
Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping
This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground
A single-chip FPGA implementation of real-time adaptive background model
This paper demonstrates the use of a single-chip
FPGA for the extraction of highly accurate background
models in real-time. The models are based
on 24-bit RGB values and 8-bit grayscale intensity
values. Three background models are presented, all
using a camcorder, single FPGA chip, four blocks
of RAM and a display unit. The architectures have
been implemented and tested using a Panasonic NVDS60B
digital video camera connected to a Celoxica
RC300 Prototyping Platform with a Xilinx Virtex
II XC2v6000 FPGA and 4 banks of onboard RAM.
The novel FPGA architecture presented has the advantages
of minimizing latency and the movement of
large datasets, by conducting time critical processes
on BlockRAM. The systems operate at clock rates
ranging from 57MHz to 65MHz and are capable
of performing pre-processing functions like temporal
low-pass filtering on standard frame size of 640X480
pixels at up to 210 frames per second
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