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Joshua Springer

dblp:316/9893 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2025
0000-0003-0137-1770ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 77% Internet of things and sensor networks · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › localization algorithms
hierarchical localization
0.912025
Reproducibility Companion Paper: Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks · ACM Multimedia 2025
Internet of things and sensor networks › cyber-physical systems
swarm robotics
0.312025
Reproducibility Companion Paper: Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks · ACM Multimedia 2025
YearPublicationVenuePosition
2025 Reproducibility Companion Paper: Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks
abstract
This companion paper provides artifacts and instructions on replicating the experiments in the ACM Multimedia 2024 paper entitled ''Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks.'' Swarm-based hierarchical, Swarical, is a localization technique that enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. It consists of two components, an offline planner and an online localization technique that executes on an FLS. The offline planner uses the FLS sensor specification for positioning to convert mesh files into swarms of FLSs. Some FLSs are dark and used only for localization. We reported the online localization technique to be fast and highly accurate. We describe how to reproduce this finding using our artifacts.
Hamed Alimohammadzadeh, Shahram Ghandeharizadeh, Federico Cunico, Joshua Springer
ACM Multimedia4
2025 Toward Appearance-Based Autonomous Landing Site Identification for Multirotor Drones in Unstructured Environments
Joshua Springer, Gylfi Þór Guðmundsson, Marcel Kyas
MMM (4)1
2024 Lowering Barriers to Entry for Fully-Integrated Custom Payloads on a DJI Matrice
abstract
Consumer-grade drones have become effective multimedia collection tools, spring-boarded by rapid development in embedded CPUs, GPUs, and cameras. They are best known for their ability to cheaply collect high-quality aerial video, 3D terrain scans, infrared imagery, etc., with respect to manned aircraft. However, users can also create and attach custom sensors, actuators, or computers, so the drone can collect different data, generate composite data, or interact intelligently with its environment, e.g., autonomously changing behavior to land in a safe way, or choosing further data collection sites. Unfortunately, developing custom payloads is prohibitively difficult for many researchers outside of engineering. We provide guidelines for how to create a sophisticated computational payload that integrates a Raspberry Pi 5 into a DJI Matrice 350. The payload fits into the Matrice's case like a typical DJI payload (but is much cheaper), is easy to build and expand (3D-printed), uses the drone's power and telemetry, can control the drone and its other payloads, can access the drone's sensors and camera feeds, and can process video and stream it to the operator via the controller in real time. We describe the difficulties and proprietary quirks we encountered, how we worked through them, and provide setup scripts and a known-working configuration for others to use.
Joshua Springer, Gylfi Þór Guðmundsson, Marcel Kyas
CBMI1