Hi, I am David Hall, research scientist at the Commonwealth Science and Industrial Research Organization (CSIRO) whose long-term goal is to see robots able to cope with the unpredictable real world.

I began this journey with my PhD on adaptable systems for autonomous weed species recognition as a part of the strategic investment in farm robotics (SIFR) team. Between 2018 and 2021 I have worked as part of the robotic vision challenge group within the Australian Centre of Robotic Vision (ACRV) and QUT Centre for Robotics designing challenges, benchmarks, and evaluation measures that assist emerging areas of robotic vision research.

As a part of the robotic vision challenge group, I have assisted in:

• Defining the field of probabilistic object detection (PrOD)

• Creating the probability-based detection quality (PDQ) evaluation measure

• Developing a PrOD robotic vision challenge

• Developing a scene understanding robotic vision challenge

I have continued my research in agricultural robotics, examining weed recognitions systems that can predict when they might fail, as well as investigations into the uses of implicit model representations for visual place recognition.

I have just begun working for CSIRO and am looking forward to producing more work that can bring sense and safety to useful autonomous systems.

Title: Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields

Authors: Stephen Hausler, David Hall, Sutharsan Mahendren, Peyman Moghadam

Year: 2024

Published in: ICRA 2024 (TBP)

Paper Link: reg-nf_arxiv

Bibtex: regnf-arxiv.bib

Note: Equal contribution with first author

Title: BenchBot environments for active robotics (BEAR): simulated data for active scene understanding research

Authors: David Hall, Ben Talbot, Suman Raj Bista, Haoyang Zhang, Rohan Smith, Feras Dayoub, Niko Suenderhauf

Year: 2022

Published in: International Journal for Robotics Research

Paper Link: https://doi.org/10.1177/02783649211069404 

Bibtex: ijrr2022.bib

Title: Probabilstic Object Detection: Definition and Evaluation

Authors: David Hall, Feras Dayoub, John Skinner, Haoyang Zhang, Dimity Miller, Peter Corke, Gustavo Carneiro, Anelia Angelova, Niko Suenderhauf

Year: 2020

Published in: 2020 IEEE Winter Conference on Applications of Computer Vision

Manuscript Download Link: WACV2020-arxiv

Bibtex: prod2020.bib