57 accepted papers
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relat…
Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address thes
Many Inertial Navigation Systems (INS) use Global Navigation Satellite System (GNSS) position as the primary measurement to drive filter performance and bound error growth. However, commercial-grade GNSS receivers introduce unknown measurement delays ranging from 50 ms to 300 ms depending on sensor …
Precise localization with respect to a set of objects of interest enables mobile robots to perform various tasks. With the rise of edge devices capable of deploying deep neural networks (DNNs) for real-time inference, it stands to reason to use artificial intelligence (AI) for the extraction of obje…
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined senso…
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the most likely sensor model for a time series of unknown measurement data, given an extendable catalog of predefined senso
This letter introduces the Cable Robot Simulation and Control (CaRoSaC) Framework, which integratesa realistic simulation environment with a model-free reinforcement learning control methodology for suspended Cable-Driven Parallel Robots (CDPRs), accounting for the effects of cable sag. Our approach
Aiming to enhance the consistency and thus long-term accuracy of Extended Kalman Filters for terrestrial vehicle localization, this paper introduces the Manifold Error State Extended Kalman Filter (M-ESEKF). By representing the robot’s pose in a space with reduced dimensionality, the approach ensure
This letter proposes a new approach for Inertial Measurement Unit (IMU) preintegration, a fundamental building block that can be leveraged in different optimization-based Inertial Navigation System (INS) localization solutions. Inspired by recent advances in equivariant theory applied to biased INSs
Using 3D point clouds in odometry estimation in robotics often requires finding a set of correspondences between points in subsequent scans. While there are established methods for point clouds of sufficient quality, state-of-the-art still struggles when this quality drops. Thus, this paper presents
This paper presents a framework for the real-time initialization of unknown Ultra-Wideband (UWB) anchors in UWB-aided navigation systems. The method is designed for localization solutions where UWB modules act as supplementary sensors. Our approach enables the automatic detection and calibration of
In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state est
The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates th…
The majority of commercial and open-source autopilot software for uncrewed aerial vehicles rely on the tried and tested extended Kalman filter (EKF) to provide the state estimation solution for the inertial navigation system (INS). While modern implementations achieve remarkable robustness, it is of…
This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">equivariant</i> filter (MSC <italic xmlns:mml="http://w
This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity – a perquisite for handling and processing meshed…
In this paper, we present a Radar-Inertial Odometry (RIO) method based on the nonlinear optimization of factor graphs in a sliding window fashion. Our method makes use of a light-weight, low-power, inexpensive and commonly available hardware enabling easy deployment on small Unmanned Aerial Vehicles…
The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of critical infrastructure with Unmanned Aerial Vehicles (UAVs), for example, requires precise navigation relative to the struc…
Modern unmanned aerial vehicles (UAVs) with sophisticated mechanics ask for extended online system identification to aid model-based controls in task execution. In addition, UAVs in adverse environmental conditions require a more detailed environmental disturbance understanding. The necessary combin…
Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework…
In this paper, we present a Radar-Inertial Odometry (RIO) approach that utilizes performance improving modules, enhanced for the sparse and noisy radar signals, from the vision community in order to estimate the full 6DoF pose and 3D velocity of a robot in an unprepared environment. Our method lever…
This paper introduces UVIO, a multi-sensor framework that leverages Ultra Wide Band (UWB) technology and Visual-Inertial Odometry (VIO) to provide robust and low-drift localization. In order to include range measurements in state estimation, the position of the UWB anchors must be known. This study…
This article presents an entirely data-driven approach for autonomous control of redundant manipulators with hydraulic actuation. The approach only requires minimal system information, which is inherited from a simulation model. The non-linear hydraulic actuation dynamics are modeled using actuator…
While low-level auto pilot stacks for aerial vehicles focus on robust control, sensing, and estimation, the continuous advancement of higher-level autonomy for aerial vehicles requires much more complex higher-level flight stacks in order to enable safe, fully autonomous long-duration missions. Rath
In this research, we aim to answer the question: How to combine Closed-Loop State and Input Sensitivity-based with Observability-aware trajectory planning? These possibly op-posite optimization objectives can be used to improve trajectory control tracking and, at the same time, estimation performanc…
Collaboratively estimating the state of two robots under communication constraints is challenging regarding computational complexity and statistical optimality. Previous work only achieves practical solutions by either disregarding parts of the measurements or imposing a communication overhead, bein…
Inertial Navigation Systems (INS) are a key technology for autonomous vehicles applications. Recent advances in estimation and filter design for the INS problem have exploited geometry and symmetry to overcome limitations of the classical Extended Kalman Filter (EKF) approach that formed the mainsta…
We present a novel approach to improve 6 degree-of-freedom state propagation for unmanned aerial vehicles in a classical filter through pre-processing of high-speed inertial data with AI algorithms. We evaluate both an LSTM-based approach as well as a Transformer encoder architecture. Both algorithm…
Suspended Cable-Driven Parallel Robots (SCDPR) have intriguing capabilities on large scales but still have open challenges in precisely estimating the end-effector pose. The cables exhibit a downward curved shape, also known as cable sag which needs to be accounted for in the pose estimation. The ca…
Stochastic filters for on-line state estimation are a core technology for autonomous systems. The performance of such filters is one of the key limiting factors to a system's capability. Both asymptotic behavior (e.g., for regular operation) and transient response (e.g., for fast initialization and
Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, clutter and occlusion, but it becomes more challenging when additional information, such as depth and 3D models, is not p…
Navigating accurately in potentially GPS-denied environments is a perquisite of autonomous systems. Relative localization based on ultra-wideband (UWB) is - especially indoors - a promising technology. In this paper, we present a probabilistic filter based Modular Multi-Sensor Fusion (MMSF) approach…
Multicopter Unmanned Aerial Vehicles (UAV) are small and agile robots with the potential to become prominent in performing autonomous tasks in various Global Navigation Satellite System (GNSS)-denied environments. These environments can potentially be rendered even more challenging due to external f…
For Ultra-Wide-Band (UWB) based navigation, an accurate initialization of the anchors in a reference coordinate system is crucial for precise subsequent UWB-inertial based pose estimation. This paper presents a strategy based on information theory to initialize such UWB anchors using raw distance me…
Precise system identification is an important aspect of adequate control design and parameter definition to allow for accurate and reliable navigation. While this is well known in robotics, the community working with small rotorcraft Unmanned Aerial Vehicles (UAVs) has yet to discover the benefits.…
This work presents the Manifold Invariant Extended Kalman Filter, a novel approach for better consistency and accuracy in state estimation on manifolds. The robustness of this filter allows for techniques with high noise potential like ultra-wideband localization to be used for a wider variety of ap…
Accurate system modeling and identification gain importance as tasks executed by autonomously acting unmanned aerial vehicles (UAVs) get more complex and demanding. This paper presents a Bayesian filter approach to online and continuously identify the system parameters; sensor suite calibration stat…
State-of-the-art recursive sensor filtering frameworks allow the fusion of multiple sensors tailored to a specific problem but do not allow a dynamic and efficient introduction of additional sensors during runtime - an important feature to enable long-term missions in dynamic environments. This lett
Monocular Visual-Inertial Odometry (VIO) has become ubiquitous for navigation of autonomous Micro Air Vehicles (MAVs). Yet, state-of-the-art VIO is still very failure-prone, which can have dramatic consequences. To prevent this, VIO must be able to re-initialize in mid-air, either during a free fall…
Fusing information from multiple sensors in aprobabilistic framework can significantly improve the estimation of the system's states, allow for redundancy, and for adaptations in dynamic environments. However, the computation complexity increases with the number of sensors and the dimension of the e
This paper presents a novel approach to recover outdated cross-covariance between correlated agents at the moment they perform joint observations. This allows to render Collaborative State Estimation (CSE) fully distributed, with communication only required for the moment of joint observation and mo…
Planning the simultaneous movement of multiple agents represents a challenging coordination problem, and ideally safety and efficiency are jointly addressed. This paper introduces a planning algorithm for fast and energy-efficient trajectories with reduced collision potential from a start to an end…
The research community presented significant advances in many different Visual-Inertial Navigation System (VINS) algorithms to localize mobile robots or hand-held devices in a 3D environment. While authors of the algorithms of-ten do compare to, at that time, existing competing approaches, their com…
Sensor fusion systems merging (multiple) delayed sensor signals through a statistical approach are challenging setups, particularly for resource constrained platforms. For statistical consistency, one would be required to keep an appropriate history, apply the correcting signal at the given time sta…
In this paper, we present a Quaternion-based Error-State Extended Kalman Filter (Q-ESEKF) based on IMU propagation with an extension for Collaborative State Estimation (CSE) and a communication complexity of O(1) (in terms of required communication links). Our approach combines a versatile filter fo…
State of the art visual-inertial odometry approaches suffer from the requirement of high gradients and sufficient visual texture. Even direct photometric approaches select a subset of the image with high-gradient areas and ignore smooth gradients or generally low-textured areas. In this work, we sho…
Delay compensation in filter based sensor fusion frameworks for multiple sensors with varying delays and different rates quickly results in large computational overhead should the delayed measurements be incorporated in a statistically meaningful way. Even more so if high rate propagation sensors (e…
We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer itera…
This paper introduces a cost function for the smoothness of a continuous periodic function, of which only some samples are given. This cost function is important e.g. when associating samples in frequency bins for problems such as analytic singular or eigenvalue decompositions. We demonstrate the ut…
Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to prev…
We propose a fully dense direct filter-based visual-inertial odometry method estimating both pixel depth for all pixels and robot state simultaneously, having all uncertainties in the same state vector. Due to the fully dense method, our approach works even in low-textured areas with very low, smoot…
We propose an approach to the throw-and-go (TnG) problem for micro air vehicles (MAVs) using visual and inertial sensors. The key challenge is the fast on-board initialization of the visual odometry (VO) system, which usually requires user input to recover the visual scale. Our approach is based on…
Many of today's Visual-Inertial Odometry (VIO)frameworks work well under regular motion but have issues and need special treatment under special motion. Here, special does not imply bad or corrupted data but stands for increased difficulty to treat clean data. Common special motion for VIO are large…
We study the nonlinear observability of a system's states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces tr
Trajectory generation approaches for mobile robots generally aim to optimize with respect to a cost function such as energy, execution time, or other mission-relevant parameters within the constraints of vehicle dynamics and obstacles in the environment. We propose to add the cost of state observabi…
Fusing data from multiple sensors on-board a mobile platform can significantly augment its state estimation abilities and enable autonomous traversals of different domains by adapting to changing signal availabilities. However, due to the need for accurate calibration and initialization of the senso
In this paper, we present a novel approach in combining visual and inertial measurements in non-static environments for first order characterization of the metric motion of non-static objects in the scene. Our approach leverages online estimated ego motion states and uses a novel inertial-optical fl…