Rudolf Mester — accepted AI-conference papers

2025

Visual Lidar Recursive Online Tracker (ViLiROT) for Autonomous Surface Vessels

Henrik Hilmarsen, Nicholas Dalhaug, Trym Anthonsen Nygård, Edmund Førland Brekke, Annette Stahl, Rudolf Mester

ICRA 2025

We propose a multi-sensor fusion pipeline for multiple object tracking in autonomous surface vessels using lidar and camera data. Our approach follows the tracking-by-detection paradigm, leveraging the precision of lidar for accurate state estimation and camera data for robust association. The metho

Cited by 1 Source ↗Scholar ↗

2022

HD Ground - A Database for Ground Texture Based Localization

Jan Fabian Schmid, Stephan F. Simon, Raaghav Radhakrishnan, Simone Frintrop, Rudolf Mester

ICRA 2022poster

We present the HD Ground Database, a comprehensive database for ground texture based localization. It contains sequences of a variety of textures, obtained using a downward facing camera. In contrast to existing databases of ground images, the HD Ground Database is larger, has a greater variety of t…

Cited by 6 Source ↗Scholar ↗

2020

Ground Texture Based Localization Using Compact Binary Descriptors

Jan Fabian Schmid, Stephan F. Simon, Rudolf Mester

ICRA 2020poster

Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of it…

Cited by 13 Source ↗Scholar ↗

2020

Ground Texture Based Localization: Do We Need to Detect Keypoints?

Jan Fabian Schmid, Stephan F. Simon, Rudolf Mester

IROS 2020poster

Localization using ground texture images recorded with a downward-facing camera is a promising approach to achieve reliable high-accuracy vehicle positioning. A common way to accomplish the task is to focus on prominent features of the ground texture such as stones and cracks. Our results indicate t…

Cited by 5 Source ↗Scholar ↗

2019

Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes

Fabian Brickwedde, Steffen Abraham, Rudolf Mester

ICCV 2019poster

Existing 3D scene flow estimation methods provide the 3D geometry and 3D motion of a scene and gain a lot of interest, for example in the context of autonomous driving. These methods are traditionally based on a temporal series of stereo images. In this paper, we propose a novel monocular 3D scene f…

Cited by 64 PDF ↗code ↗Scholar ↗

2018

Mono-Stixels: Monocular Depth Reconstruction of Dynamic Street Scenes

Fabian Brickwedde, Steffen Abraham, Rudolf Mester

ICRA 2018poster

In this paper we present mono-stixels, a compact environment representation specially designed for dynamic street scenes. Mono-stixels are a novel approach to estimate stixels from a monocular camera sequence instead of the traditionally used stereo depth measurements. Our approach jointly infers th…

Cited by 8 Source ↗Scholar ↗

2016

Lost and Found: detecting small road hazards for self-driving vehicles

Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, Rudolf Mester

IROS 2016poster

Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle.

Cited by 217 Source ↗Scholar ↗

2015

High-performance long range obstacle detection using stereo vision

Peter Pinggera, Uwe Franke, Rudolf Mester

IROS 2015poster

Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The ap…

Cited by 35 Source ↗Scholar ↗