People-Sensing Spatial Characteristics of RF Sensor Networks

An "RF sensor" network can monitor RSS values on links in the network and perform device-free localization, i.e., locating a person or object moving in the area in which the network is deployed. This paper provides a statistical model for the RSS variance as a function of the person's position w.r.t. the transmitter (TX) and receiver (RX). We show that the ensemble mean of the RSS variance has an approximately linear relationship with the expected total affected power (ETAP). We then use analysis to derive approximate expressions for the ETAP as a function of the person's position, for both scattering and reflection. Counterintuitively, we show that reflection, not scattering, causes the RSS variance contours to be shaped like Cassini ovals. Experimental tests reported here and in past literature are shown to validate the analysis

Reasoning From Point Clouds

Over the past two years, 3D object detection has been a major area of focus across industry and academia. This is primarily due to the difficulty of learning data from point clouds. While camera images are fixed size and can therefore be easily trained on using convolution, point clouds are unstructured series of points in three dimensions. Therefore, there is no fixed number of features, or a structure to run convolution on. Instead, researchers have developed many ways of attempting to learn from this data, however there is no clear consensus on what is the best method, as each has advantages and disadvantages. For this project, I chose to focus on understanding and implementing VoxelNet, a voxelized method for object detection using point cloud data. I used the VoxelNet architecture for the task of detecting objects in the surrounding environment and creating 3D bounding boxes around those objects. I trained these models on the Waymo Open Dataset, then measured performance on the Carla simulator. The goal of training on the Waymo Open Dataset was to gain experience with the new dataset and familiarity with its features, and then evaluate the practicality of the Carla simulator by using a model trained with real-world data in it

ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty

In this paper, we develop a modular neural network for real-time semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer. Our approach combines the reliability of classical probabilistic algorithms with the performance and efficiency of modern neural networks. Although robotic perception is often divided between modern differentiable methods and classical explicit methods, a union of both is necessary for real-time and trustworthy performance. We introduce a novel Convolutional Bayesian Kernel Inference (ConvBKI) layer which incorporates semantic segmentation predictions online into a 3D map through a depthwise convolution layer by leveraging conjugate priors. We compare ConvBKI against state-of-the-art deep learning approaches and probabilistic algorithms for mapping to evaluate reliability and performance. We also create a Robot Operating System (ROS) package of ConvBKI and test it on real-world perceptually challenging off-road driving data.Comment: arXiv admin note: text overlap with arXiv:2209.1066

Convolutional Bayesian Kernel Inference for 3D Semantic Mapping

Robotic perception is currently at a cross-roads between modern methods which operate in an efficient latent space, and classical methods which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (ConvBKI) layer which explicitly performs Bayesian inference within a depthwise separable convolution layer to simultaneously maximize efficiency while maintaining reliability. We apply our layer to the task of 3D semantic mapping, where we learn semantic-geometric probability distributions for LiDAR sensor information in real time. We evaluate our network against state-of-the-art semantic mapping algorithms on the KITTI data set, and demonstrate improved latency with comparable semantic results

The Grizzly, April 3, 1981

Board of Directors Elects Corey to Five Year Term • Fire Alarm and Damage Plague Beta Sig • Parents Day Packed With Fun • Resident Assistants Announced • Reagan\u27s Programs • Departmental Focus: Political Science • Music News • Transplanted Texan: Cult of Violence • Franken and Davis Bring Saturday Night to Bomberger • ProTheatre\u27s Dream Fresh and Funny • Pennsylvania Folk Art • Chamber Orchestra Salutes Bach • History Department Sponsors Phillies Contest • Men\u27s Lacrosse Off to Slow Start • Thinclads Just Off F&M\u27s Mark • Lacrosse 4th in Nation • Baseball Sweeps F&Mhttps://digitalcommons.ursinus.edu/grizzlynews/1057/thumbnail.jp

Morgan Papers: Exploring the Correspondence of California’s First Female Architect

Descriptive metadata and full-text transcripts have long been valued for their roles in powering search engines and faceted browsing. But as the morganpapers.org web application demonstrates, such textual data (both structured and unstructured) can be leveraged to build a variety of tools which provide deeper and broader insight than simple searching and browsing. The Robert E. Kennedy Library at Cal Poly recently completed digitization of a unique body of correspondence between architect Julia Morgan and William Randolph Hearst, carried out during the construction of what is now known as Hearst Castle. The structure is a masterpiece and the crown jewel of Morgan’s illustrious career throughout California, where she worked as the state’s first female licensed architect. The collection consists of over 2,500 letters, telegrams, notes, and other documents (totalling over 3,200 pages), spanning the years 1919-1941. The pieces were written in several places across the United States and overseas. As each piece of correspondence was digitized, it was ingested in the library's archival repository along with its MODS-based metadata, and full-text transcripts (for both typescripts and manuscripts)

The Grizzly, April 10, 1981

Sigma Pi Sigma Chapter Comes to the Campus • Men Draw for Rooms Thursday • College Choir to Present The Creation April 11 • Gulf Oil Aids Students • Lindback Nominations Requested by Dean • Saturday Night Live de Espanol • Cub and Key Selected • Co-ed Housing: Is it Possible at Ursinus? • Counseling Services in Collegeville • Ursinus Astronomy Forum • Departmental Focus: Psychology; German • Music News • Transplanted Texan • Raykes Deserve More Attention • Paradise Theatre Reopens in Philly • Portrait Schedule Announced • Platforms for Class Office Candidates • Sports Profile: Rob Randelman • Women\u27s Lacrosse • Track Runs Away With Another Perfect Week • Baseball Looking Good • Men\u27s Lacrosse Wins in Overtimehttps://digitalcommons.ursinus.edu/grizzlynews/1058/thumbnail.jp