arXiv subjects

Andrew Fishberg

Publications and source records attributed to Andrew Fishberg.

6 recordsLinked to original sources

arXiv2026

CILC: Cryptographically-secure Inter-agent Loop Closure Candidate Detection for Multi-Agent Collaborative SLAM

Multi-agent Simultaneous Localization and Mapping (SLAM) and collaborative SLAM (CSLAM) require robots to continuously exchange global descriptors (GDs) to detect inter-agent loop closures (ILCs). While encrypted radios protect this traffic from external eavesdroppers, they offer no protection against a compromised swarm member. We show this threat is concrete by demonstrating how a corrupted agent can reconstruct approximations of an honest agent's imagery and trajectory from its public GD broadcasts. To address this, we propose CILC (Cryptographically-secure Inter-agent Loop Closure candidate detection), a first-of-its-kind system leveraging Secure Multi-Party Computation (SMPC) to detect ILC candidates without exchanging GDs in the clear. Rather than securing the entire CSLAM pipeline, we apply SMPC only to ILC candidate detection (i.e., GD similarity comparison), a privacy-sensitive yet computationally lightweight step, yielding an advantageous privacy-to-overhead trade-off. We validate in both simulation and hardware experiments that CILC remains real-time and communication-feasible across multimodal GDs (visual and LiDAR), while mitigating information leakage to a compromised swarm agent.

cs.RO↗

arXiv2024

From Prisons to Programming: Fostering Self-Efficacy via Virtual Web Design Curricula in Prisons and Jails

Self-efficacy and digital literacy are key predictors to incarcerated people's success in the modern workplace. While digitization in correctional facilities is expanding, few templates exist for how to design computing curricula that foster self-efficacy and digital literacy in carceral environments. As a result, formerly incarcerated people face increasing social and professional exclusion post-release. We report on a 12-week college-accredited web design class, taught virtually and synchronously, across 5 correctional facilities across the United States. The program brought together men and women from gender-segregated facilities into one classroom to learn fundamentals in HTML, CSS and Javascript, and create websites addressing social issues of their choosing. We conducted surveys with participating students, using dichotomous and open-ended questions, and performed thematic and quantitative analyses of their responses that suggest students' increased self-efficacy. Our study discusses key design choices, needs, and recommendations for furthering computing curricula that foster self-efficacy and digital literacy in carceral settings.

cs.HC↗

arXiv2023

MURP: Multi-Agent Ultra-Wideband Relative Pose Estimation with Constrained Communications in 3D Environments

Inter-agent relative localization is critical for many multi-robot systems operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 3D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data (e.g., odometry) leading to potential scaling issues with increased team size and/or decreased communication network capability. By equipping each agent with multiple UWB antennas, our approach addresses these concerns by using only locally collected UWB range measurements, a priori state constraints, and event-based detections of when said constraints are violated. The addition of our learned mean ranging bias correction improves our approach by an additional 19% positional error, and gives us an overall experimental mean absolute position and heading errors of 0.24m and 9.5 degrees respectively. When compared to other state-of-the-art approaches, our work demonstrates improved performance over similar systems, while remaining competitive with methods that have significantly higher communication costs. Additionally, we make our datasets available.

cs.RO↗

arXiv2023

Certifiably Correct Range-Aided SLAM

We present the first algorithm to efficiently compute certifiably optimal solutions to range-aided simultaneous localization and mapping (RA-SLAM) problems. Robotic navigation systems increasingly incorporate point-to-point ranging sensors, leading to state estimation problems in the form of RA-SLAM. However, the RA-SLAM problem is significantly more difficult to solve than traditional pose-graph SLAM: ranging sensor models introduce non-convexity and single range measurements do not uniquely determine the transform between the involved sensors. As a result, RA-SLAM inference is sensitive to initial estimates yet lacks reliable initialization techniques. Our approach, certifiably correct RA-SLAM (CORA), leverages a novel quadratically constrained quadratic programming (QCQP) formulation of RA-SLAM to relax the RA-SLAM problem to a semidefinite program (SDP). CORA solves the SDP efficiently using the Riemannian Staircase methodology; the SDP solution provides both (i) a lower bound on the RA-SLAM problem's optimal value, and (ii) an approximate solution of the RA-SLAM problem, which can be subsequently refined using local optimization. CORA applies to problems with arbitrary pose-pose, pose-landmark, and ranging measurements and, due to using convex relaxation, is insensitive to initialization. We evaluate CORA on several real-world problems. In contrast to state-of-the-art approaches, CORA is able to obtain high-quality solutions on all problems despite being initialized with random values. Additionally, we study the tightness of the SDP relaxation with respect to important problem parameters: the number of (i) robots, (ii) landmarks, and (iii) range measurements. These experiments demonstrate that the SDP relaxation is often tight and reveal relationships between graph connectivity and the tightness of the SDP relaxation.

cs.RO↗

arXiv2022

Multi-Agent Relative Pose Estimation with UWB and Constrained Communications

Inter-agent relative localization is critical for any multi-robot system operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 2D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data, such as odometry, making these approaches scale poorly with increased swarm size or decreased communication throughput. This approach addresses these concerns by using only locally collected UWB measurements with no additionally transmitted data. By modeling observed ranging biases and systematic antenna obstructions in our proposed optimization solution, our experimental results demonstrate an improved mean position error (while remaining competitive in other metrics) over a similar state-of-the-art approach that additionally relies on continuously transmitted odometry.

cs.RO↗

arXiv2015

An Approach to Data Prefetching Using 2-Dimensional Selection Criteria

We propose an approach to data memory prefetching which augments the standard prefetch buffer with selection criteria based on performance and usage pattern of a given instruction. This approach is built on top of a pattern matching based prefetcher, specifically one which can choose between a stream, a stride, or a stream followed by a stride. We track the most recently called instructions to make a decision on the quantity of data to prefetch next. The decision is based on the frequency with which these instructions are called and the hit/miss rate of the prefetcher. In our approach, we separate the amount of data to prefetch into three categories: a high degree, a standard degree and a low degree. We ran tests on different values for the high prefetch degree, standard prefetch degree and low prefetch degree to determine that the most optimal combination was 1, 4, 8 lines respectively. The 2 dimensional selection criteria improved the performance of the prefetcher by up to 9.5% over the first data prefetching championship winner. Unfortunately performance also fell by as much as 14%, but remained similar on average across all of the benchmarks we tested.

cs.AR↗