Leonard Bruns

I am a PhD candidate in robotics and computer vision supervised by Patric Jensfelt (started October 2019).

My main research interests are at the intersection of robotics and computer vision (and to a lesser degree computer graphics). I am particularly interested in the development of algorithms for robot perception such as pose and shape estimation of objects from partial information, dense SLAM, and visual localization.

Neural Graph Mapping for Dense SLAM with Efficient Loop Closure

Leonard Bruns, Jun Zhang, Patric Jensfelt

arXiv, 2024

pdf | arXiv | Project Page | Github

Transitional Grid Maps: Efficient Analytical Inference of Dynamic Environments under Limited Sensing

José Manuel Gaspar Sánchez, Leonard Bruns, Jana Tumova, Patric Jensfelt, Martin Törngren

arXiv, 2024

arXiv

RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation

Leonard Bruns, Patric Jensfelt

Robotics and Autonomous Systems 168, 2023

pdf | arXiv | Elsevier | Github

A Probabilistic Framework for Visual Localization in Ambiguous Scenes

Fereidoon Zangeneh, Leonard Bruns, Amit Dekel, Alessandro Pieropan, Patric Jensfelt

IEEE International Conference on Robotics and Automation (ICRA), 3969-3975, 2023

pdf | arXiv | IEEE | Github

SDF-Based RGB-D Camera Tracking in Neural Scene Representations

Leonard Bruns, Fereidoon Zangeneh, Patric Jensfelt

IEEE ICRA Workshop on Motion Planning with Implicit Neural Representations of Geometry, 2022

pdf | arXiv

SDFEst: Categorical Pose and Shape Estimation of Objects From RGB-D Using Signed Distance Fields

Leonard Bruns, Patric Jensfelt

IEEE Robotics and Automation Letters 7 (4), 9597-9604, 2022

pdf | arXiv | IEEE | Github

On the Evaluation of RGB-D-Based Categorical Pose and Shape Estimation

Leonard Bruns, Patric Jensfelt

Intelligent Autonomous Systems 17 (IAS-17), 2022

pdf | arXiv | Springer | Github

Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots

Eric Heiden^*, Luigi Palmieri^*, Leonard Bruns, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig

IEEE Robotics and Automation Letters 6 (3), 4536-4543, 2020

IEEE | Github

Dispertio: Optimal Sampling For Safe Deterministic Motion Planning

Luigi Palmieri^*, Leonard Bruns^*, Michael Meurer, Kai Oliver Arras

IEEE Robotics and Automation Letters 5 (2), 362-368, 2019

pdf | arXiv | IEEE

Deterministic Sampling-Based Motion Planning

Leonard Bruns

Master's Thesis, 2019

pdf

Leonard Bruns

Projects

Neural Graph Mapping for Dense SLAM with Efficient Loop Closure

Transitional Grid Maps: Efficient Analytical Inference of Dynamic Environments under Limited Sensing

RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation

A Probabilistic Framework for Visual Localization in Ambiguous Scenes

SDF-Based RGB-D Camera Tracking in Neural Scene Representations

SDFEst: Categorical Pose and Shape Estimation of Objects From RGB-D Using Signed Distance Fields

On the Evaluation of RGB-D-Based Categorical Pose and Shape Estimation

Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots

Dispertio: Optimal Sampling For Safe Deterministic Motion Planning

Deterministic Sampling-Based Motion Planning