Simon Schaefer — accepted AI-conference papers

2026

FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment

Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou, Simon Schaefer, Jaehyung Jung, Helen Oleynikova +1

ICRA 2026poster

Geometrically accurate and semantically expressive map representations have proven invaluable for robot deployment and task planning in unknown environments. Nevertheless, real-time, open-vocabulary semantic understanding of large-scale unknown environments still presents open challenges, mainly due…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2026

HumanFlow - Diffusion-Driven MAV Navigation Among Humans via Tightly-Coupled Motion Tracking, Forecasting, and Control

Simon Schaefer, Joshua Näf, Stefan Leutenegger

RSS 2026poster

Robust and accurate perception of humans in their 3D scene context is essential for integrating robots into everyday environments. Existing approaches, however, often fail to predict plausible and accurate human motion estimates that are consistent with the surrounding scene, especially in the prese…

Cited by 0 Source ↗Scholar ↗

2025

Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR

Sebastián Barbas Laina, Simon Boche, Sotiris Papatheodorou, Dimos Tzoumanikas, Simon Schaefer, Hanzhi Chen +1

IROS 2025

Autonomous navigation is needed for several robotics applications. In this paper we present an autonomous Micro Aerial Vehicle (MAV) system which purely relies on cost-effective and light-weight passive visual and inertial sensors to perform large-scale autonomous navigation in outdoor, unstructured

Cited by 3 Source ↗code ↗Scholar ↗

2025

SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM

Yannick Burkhardt, Simon Schaefer, Stefan Leutenegger

ICCV 2025poster

Event-based keypoint detection and matching holds significant potential, enabling the integration of event sensors into highly optimized Visual SLAM systems developed for frame cameras over decades of research. Unfortunately, existing approaches struggle with the motion-dependent appearance of keypo…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

Online Tree Reconstruction and Forest Inventory on a Mobile Robotic System

Leonard Freißmuth, Matias Mattamala, Nived Chebrolu, Simon Schaefer, Stefan Leutenegger, Maurice Fallon

IROS 2024poster

Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing…

Cited by 8 Source ↗Scholar ↗

2023

A Benchmark for Multi-Robot Planning in Realistic, Complex and Cluttered Environments

Simon Schaefer, Luigi Palmieri, Lukas Heuer, Ruediger Dillmann, Sven Koenig, Alexander Kleiner

ICRA 2023poster

Several successful approaches exist for solving the complex problem of multi-robot planning and coordination. Due to the lack of adequate benchmarking tools, comparing these approaches and judging their suitability for use in realistic scenarios is currently difficult. Therefore, we propose an open-…

Cited by 4 Source ↗Scholar ↗

2023

BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking

Dorian F. Henning, Christopher Choi, Simon Schaefer, Stefan Leutenegger

IROS 2023poster

Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in realtime is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state…

Cited by 12 Source ↗Scholar ↗

2023

GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild

Simon Schaefer, Dorian F. Henning, Stefan Leutenegger

IROS 2023poster

An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions. Current uncertainty-aware methods in 3D human pose estimation are limited to predicting the uncertainty of the body posture, while effectively neglecting the body sha…

Cited by 3 Source ↗Scholar ↗

2022

AEGNN: Asynchronous Event-Based Graph Neural Networks

Simon Schaefer, Daniel Gehrig, Davide Scaramuzza

CVPR 2022poster

The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and high temporal resolution of events, leading to high computational burden and la…

Cited by 162 PDF ↗Scholar ↗

2022

Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion

Simon Boche, Xingxing Zuo, Simon Schaefer, Stefan Leutenegger

IROS 2022poster

Robotic applications are continuously striving towards higher levels of autonomy. To achieve that goal, a highly robust and accurate state estimation is indispensable. Combining visual and inertial sensor modalities has proven to yield accurate and locally consistent results in short-term applicatio…

Cited by 20 Source ↗Scholar ↗

2021

Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions

Simon Schaefer, Karen Leung, Boris Ivanovic, Marco Pavone

ICRA 2021poster

To achieve seamless human-robot interactions, robots need to intimately reason about complex interaction dynamics and future human behaviors within their motion planning process. However, there is a disconnect between state-of-the-art neural network-based human behavior models and robot motion plann…

Cited by 36 Source ↗code ↗Scholar ↗