Author name cluster

Jacques Kaiser

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3 papers

2 author rows

ICRA Conference 2025 Conference Paper

MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models

Johannes Meier
Louis Inchingolo
Oussema Dhaouadi
Yan Xia 0003
Jacques Kaiser
Daniel Cremers

We tackle the problem of monocular 3D object detection across different sensors, environments, and camera setups. In this paper, we introduce a novel unsupervised domain adaptation approach, MonoCT, that generates highly accurate pseudo labels for self-supervision. Inspired by our observation that accurate depth estimation is critical to mitigating domain shifts, MonoCT introduces a novel Generalized Depth Enhancement (GDE) module with an ensemble concept to improve depth estimation accuracy. Moreover, we introduce a novel Pseudo Label Scoring (PLS) module by exploring inner-model consistency measurement and a Diversity Maximization (DM) strategy to further generate high-quality pseudo labels for self-training. Extensive experiments on six benchmarks show that MonoCT outperforms existing SOTA domain adaptation methods by large margins (~21% minimum for AP Mod.) and generalizes well to car, traffic camera and drone views.

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NeurIPS Conference 2025 Conference Paper

OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata

Oussema Dhaouadi
Riccardo Marin
Johannes Meier
Jacques Kaiser
Daniel Cremers

Accurate visual localization from aerial views is a fundamental problem with applications in mapping, large-area inspection, and search-and-rescue operations. In many scenarios, these systems require high-precision localization while operating with limited resources (e. g. , no internet connection or GNSS/GPS support), making large image databases or heavy 3D models impractical. Surprisingly, little attention has been given to leveraging orthographic geodata as an alternative paradigm, which is lightweight and increasingly available through free releases by governmental authorities (e. g. , the European Union). To fill this gap, we propose OrthoLoC, the first large-scale dataset comprising 16, 425 UAV images from Germany and the United States with multiple modalities. The dataset addresses domain shifts between UAV imagery and geospatial data. Its paired structure enables fair benchmarking of existing solutions by decoupling image retrieval from feature matching, allowing isolated evaluation of localization and calibration performance. Through comprehensive evaluation, we examine the impact of domain shifts, data resolutions, and covisibility on localization accuracy. Finally, we introduce a refinement technique called AdHoP, which can be integrated with any feature matcher, improving matching by up to 95% and reducing translation error by up to 63%. The dataset and code are available at: https: //deepscenario. github. io/OrthoLoC.

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IROS Conference 2019 Conference Paper

Combining spiking motor primitives with a behaviour-based architecture to model locomotion for six-legged robots

Juan Camilo Vasquez Tieck
Jacqueline Rutschke
Jacques Kaiser
Martin Schulze
Timothee Buettner
Daniel Reichard
Arne Roennau
Rüdiger Dillmann

Bio-inspired robots take advantage of millions of years of evolution to provide interesting and flexible solutions for issues related to motion and perception. Often, they have challenging kinematics classical robotics control mechanisms are not always able to take advantage of them. A concrete example of this is LAURON V, a six-legged robot for space exploration inspired by the stick insects. The main goals of this work is to combine classical behaviour-based control with motor primitives implemented with SNN for motion representation. We extend a previously presented bio-inspired approach to represent hand and arm motion using motor primitives, and combine it with a behaviour-based architecture to model different locomotion behaviours for a multi-legged robot. There are four main components. First, to model the individual leg motions we use two motor primitives implemented with spiking neural networks for the swing and stance phases. Second, to control the motor primitives of each leg there are local behaviours corresponding to each phase, and corresponding to each activation pattern. Third, the activation patterns are used to facilitate multi-leg coordination and generate different walking behaviours. Fourth, a high-level control interface integrates control signals from other sources and activates the patterns. We conducted five different experiments to evaluate our approach in a simulated environment using the Neurorobotics Platform (NRP). The results show that our modelling approach with motor primitives is flexible enough to represent different types of motions, and also highlight the value of the NRP for robotics development.

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