Matthew Lisondra

dblp:372/2781 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 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.

Artificial intelligence
1 paper
Robot navigation and mapping · 62% 3D vision · 38%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
pose estimation
0.812024
Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · ICRA 2024
Robotics › Robot navigation and mapping › visual odometry
visual-inertial odometry
0.812024
Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · ICRA 2024
Robotics › Robot navigation and mapping
localization
0.212024
Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · ICRA 2024
Robotics › Robot navigation and mapping › localization
odometry
0.212024
Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · ICRA 2024

Methods — techniques the papers use, named apart from their topics

focal-plane sensor-processor · 0.8extended kalman filter · 0.8SIMD · 0.8
YearPublicationVenuePosition
2024 Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)
abstract
Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane – at individual pixels – enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing and are based on the Single Instruction Multiple Data (SIMD) paradigm. In this paper, we present BIT-VIO, the first Visual Inertial Odometry (VIO) which utilises SCAMP-5. BIT-VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 Hz IMU measurements to provide accurate and smooth trajectories. Project Page: https://sites.google.com/view/bit-vio/home
Matthew Lisondra, Junseo Kim, Riku Murai, Kourosh Zareinia, Sajad Saeedi G.
ICRA1