47,249 research outputs found
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
Size, weight, and power constrained platforms impose constraints on
computational resources that introduce unique challenges in implementing
localization algorithms. We present a framework to perform fast localization on
such platforms enabled by the compressive capabilities of Gaussian Mixture
Model representations of point cloud data. Given raw structural data from a
depth sensor and pitch and roll estimates from an on-board attitude reference
system, a multi-hypothesis particle filter localizes the vehicle by exploiting
the likelihood of the data originating from the mixture model. We demonstrate
analysis of this likelihood in the vicinity of the ground truth pose and detail
its utilization in a particle filter-based vehicle localization strategy, and
later present results of real-time implementations on a desktop system and an
off-the-shelf embedded platform that outperform localization results from
running a state-of-the-art algorithm on the same environment
A committee machine gas identification system based on dynamically reconfigurable FPGA
This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors
- …