3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information. In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.

Robust autonomous navigation in environments with limited visibility remains a critical challenge in robotics. We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation. Our method enables mobile robots to “see around corners” by utilizing multi-bounce light information, effectively expanding their perceptual range without additional infrastructure. We propose a three-module pipeline: (1) Sensing, which captures multi-bounce histograms using SPAD-based LiDAR; (2) Perception, which estimates occupancy maps of hidden regions from these histograms using a convolutional neural network; and (3) Control, which allows a robot to follow safe paths based on the estimated occupancy. We evaluate our approach through simulations and real-world experiments on a mobile robot navigating an L-shaped corridor with hidden obstacles. Our work represents the first experimental demonstration of NLOS imaging for autonomous navigation, paving the way for safer and more efficient robotic systems operating in complex environments. We also contribute a novel dynamics-integrated transient rendering framework for simulating NLOS scenarios, facilitating future research in this domain.

Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to two-dimensional (2D) slices and require destructively slicing the object. Recovering the internal three-dimensional (3D) stress distribution of the entire object is challenging, as it involves solving a tensor tomography problem and handling phase wrapping ambiguities. We introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress tensor fields as neural implicit representations from polarization measurements. Our key insight is to jointly handle phase unwrapping and tensor tomography using a differentiable forward model based on Jones calculus. Our non-linear model faithfully matches real captures, unlike prior linear approximations. We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions. Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing photoelastic fringes from unseen viewpoints. NeST paves the way for scalable non-destructive 3D photoelastic analysis.

This contribution is concerned with the topic of using simulation to understand the behavior of groups of mutually interacting autonomous vehicles (AVs) or robots engaged in traffic/maneuvers that involve coordinated operation. We outline the structure of a multi-agent simulator called SYN-CHRONO and provide results pertaining to its scalability and ability to run real-time scenarios with humans in the loop. SYN-CHRONO is a scalable multi-agent, high-fidelity environment whose purpose is that of testing AV and robot control strategies. Four main components make up the core of the simulation platform: a physics-based dynamics engine that can simulate rigid and compliant systems, fluid-solid interactions, and deformable terrains; a module that provides sensing simulation; an agent-to-agent communication server; dynamic virtual worlds, which host the interacting agents operating in a coordinated scenario. The platform provides a virtual proving ground that can be used to answer questions such as "what will an AV do when it skids on a patch of ice and moves one way while facing the other way?"; "is a new agent-control strategy robust enough to handle unforeseen circumstances?"; and "what is the effect of a loss of communication between agents engaged in a coordinated maneuver?". Full videos based on work in the paper are available at https://tinyurl.com/ChronoIROS2020 and additional descriptions on the particular version of software used is available at https://github.com/uwsbel/publications-data/tree/master/2020/IROS.

In this work we quantified the in vitro calibration relationships between high frequency electrical stimulation and GABA and glutamate release in both mature retinoic acid differentiated P19 neurons and immortalized embryonic cortical cells engineered to express glutamic acid decarboxylase, GAD65. Extracellular glutamate and GABA was quantified by 2D gas chromatography and time of flight mass spectrometry after stimulation at varying amplitudes and frequencies. Amplitude sweeps resulted in a linear calibration for P19 neurons; the level of neurotransmitter varied over one order of magnitude from ~ 200 pg/neuron to ~ 1.2 ng/neuron for glutamate and ~ 1 ng/neuron to ~ 2 ng/neuron for GABA, depending on the stimulation amplitude. Frequency sweeps resulted in a peak release at 250 Hz for glutamate and 400 Hz for GABA in P19 cells. The GABA transporter inhibitor, nipecotic acid, increased extracellular GABA levels and decrease glutamate. In contrast the embryonic cortical cells had a strongly nonlinear dependency of release on stimulation amplitude, and a weak dependence on frequency. These cells had roughly equal extracellular glutamate and GABA levels after stimulation despite the expression of GAD65. In addition glutamate and GABA levels were insensitive to nipecotic acid. These results demonstrate an ability to calibrate and tune neurotransmitter release from neural cells using high frequency stimulation parameters.