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James R. McBride

Publications and source records attributed to James R. McBride.

3 recordsLinked to original sources

Multi-resonant non-dispersive infrared gas sensing: breaking the selectivity and sensitivity tradeoff

In applications such as atmospheric monitoring of greenhouse gases and pollutants, the detection and identification of trace concentrations of harmful gases is commonly achieved using non-dispersive infrared (NDIR) sensors. These devices employ a broadband infrared emitter, thermopile detector, and a spectrally selective bandpass filter tuned to the vibrational resonance of the target analyte. However, the fabrication of these filters is costly and limited to a single frequency. This limitation introduces a fundamental tradeoff, as broadening the optical passband width enhances sensitivity but compromises selectivity, whereas narrowing improves selectivity at the expense of sensitivity. In this work, we validate a filterless NDIR approach using a multi-peak thermal emitter developed through inverse design. This emitter enhances detection sensitivity by targeting multiple absorption bands, demonstrated through the creation of a sensor designed for the C-H vibrational modes of propane. Additionally, a set of single-peak emitters were developed to showcase the capability of designing highly selective sensors operating within close spectral proximity. These emitters, targeting the stretching modes of carbon monoxide and carbon dioxide, exhibit Q-factors above 50 and minimal crosstalk, enabling accurate detection of the target gas without interference from gases with spectrally adjacent absorption bands. This is enabled by the implementation of an aperiodic distributed Bragg reflectors, which allows for higher Q-factors with fewer layers than a periodic Bragg reflector using the same materials and number of layers, thereby reducing fabrication complexity and cost. Experimental results validate that this approach breaks the tradeoff between sensitivity and selectivity. This work highlights the potential of optimized thermal emitters for more efficient and compact gas sensing applications.

cond-mat.mes-hall↗

Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.

cs.CV↗

Robust and Fast 3D Scan Alignment using Mutual Information

This paper presents a mutual information (MI) based algorithm for the estimation of full 6-degree-of-freedom (DOF) rigid body transformation between two overlapping point clouds. We first divide the scene into a 3D voxel grid and define simple to compute features for each voxel in the scan. The two scans that need to be aligned are considered as a collection of these features and the MI between these voxelized features is maximized to obtain the correct alignment of scans. We have implemented our method with various simple point cloud features (such as number of points in voxel, variance of z-height in voxel) and compared the performance of the proposed method with existing point-to-point and point-to- distribution registration methods. We show that our approach has an efficient and fast parallel implementation on GPU, and evaluate the robustness and speed of the proposed algorithm on two real-world datasets which have variety of dynamic scenes from different environments.

cs.RO↗