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Olov Andersson

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17 papers

2 author rows

AAAI Conference 2026 Conference Paper

HiMo: High-Speed Objects Motion Compensation in Point Clouds (Abstract Reprint)

Qingwen Zhang
Ajinkya Khoche
Yi Yang
Li Ling
Sina Sharif Mansouri
Olov Andersson
Patric Jensfelt

LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused by ego motion, distortions caused by other moving objects remain largely overlooked, leading to errors in object shape and position. This distortion is particularly pronounced in high-speed environments such as highways and in multi-LiDAR configurations, a common setup for heavy vehicles. To address this challenge, we introduce HiMo, a pipeline that repurposes scene flow estimation for non-ego motion compensation, correcting the representation of dynamic objects in point clouds. We further propose SeFlow++, a real-time scene flow estimator that achieves state-of-the-art performance on both scene flow and motion compensation. We validate HiMo through extensive experiments on Argoverse 2, ZOD and a newly collected real-world dataset featuring highway driving and multi-LiDAR-equipped heavy vehicles.

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NeurIPS Conference 2025 Conference Paper

DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method

Qingwen Zhang
Xiaomeng Zhu
Yushan Zhang
Yixi Cai
Olov Andersson
Patric Jensfelt

Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grows. To leverage temporal information more efficiently, we propose DeltaFlow ($\Delta$Flow), a lightweight 3D framework that captures motion cues via a $\Delta$ scheme, extracting temporal features with minimal computational cost, regardless of the number of frames. Additionally, scene flow estimation faces challenges such as imbalanced object class distributions and motion inconsistency. To tackle these issues, we introduce a Category-Balanced Loss to enhance learning across underrepresented classes and an Instance Consistency Loss to enforce coherent object motion, improving flow accuracy. Extensive evaluations on the Argoverse 2, Waymo and nuScenes datasets show that $\Delta$Flow achieves state-of-the-art performance with up to 22\% lower error and $2\times$ faster inference compared to the next-best multi-frame supervised method, while also demonstrating a strong cross-domain generalization ability. The code is open-sourced at https: //github. com/Kin-Zhang/DeltaFlow along with trained model weights.

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ICRA Conference 2025 Conference Paper

Flora: Sample-Efficient Preference-Based Rl Via Low-Rank Style Adaptation of Reward Functions

Daniel Marta
Simon Holk
Miguel Vasco
Jens Lundell
Timon Homberger
Finn Lukas Busch
Olov Andersson
Danica Kragic

Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user preferences while still being able to perform the original task. However, collecting preferences for the adaptation process in robotics is often challenging and time-consuming. In this work we explore the adaptation of pre-trained robots in the low-preference-data regime. We show that, in this regime, recent adaptation approaches suffer from catastrophic reward forgetting (CRF), where the updated reward model overfits to the new preferences, leading the agent to become unable to perform the original task. To mitigate CRF, we propose to enhance the original reward model with a small number of parameters (low-rank matrices) responsible for modeling the preference adaptation. Our evaluation shows that our method can efficiently and effectively adjust robotic behavior to human preferences across simulation benchmark tasks and multiple real-world robotic tasks. We provide videos of our results and source code at https://sites.google.com/view/preflora/.

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ICRA Conference 2025 Conference Paper

One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation

Finn Lukas Busch
Timon Homberger
Jesús Ortega-Peimbert
Quantao Yang
Olov Andersson

The capability to efficiently search for objects in complex environments is fundamental for many real-world robot applications. Recent advances in open-vocabulary vision models have resulted in semantically-informed object navigation methods that allow a robot to search for an arbitrary object without prior training. However, these zero-shot methods have so far treated the environment as unknown for each consecutive query. In this paper we introduce a new benchmark for zero-shot multi-object navigation, allowing the robot to leverage information gathered from previous searches to more efficiently find new objects. To address this problem we build a reusable open-vocabulary feature map tailored for real-time object search. We further propose a probabilistic-semantic map update that mitigates common sources of errors in semantic feature extraction and leverage this semantic uncertainty for informed multi-object exploration. We evaluate our method on a set of object navigation tasks in both simulation as well as with a real robot, running in real-time on a Jetson Orin AGX. We demonstrate that it outperforms existing state-of-the-art approaches both on single and multi-object navigation tasks. Additional videos, code and the multi-object navigation benchmark will be available on https://finnbsch.github.io/OneMap.

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ICRA Conference 2024 Conference Paper

COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry

Patrick Pfreundschuh
Helen Oleynikova
Cesar Cadena 0001
Roland Siegwart
Olov Andersson

We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information from LiDAR intensity with geometry-based point cloud registration. The focus of our work is to improve the robustness of LiDAR-inertial odometry in geometrically degenerate scenarios, like tunnels or flat fields. We project LiDAR intensity returns into an image, and present a novel image processing pipeline that produces filtered images with improved brightness consistency within the image as well as across different scenes. We effectively leverage intensity as an additional modality, using our new feature selection scheme that detects uninformative directions in the point cloud registration and explicitly selects patches with complementary image information. Photometric error minimization in the image patches is then fused with inertial measurements and point-to-plane registration in an iterated Extended Kalman Filter. The proposed approach improves accuracy and robustness on a public dataset. We additionally publish a new dataset, that captures five real-world environments in challenging, geometrically degenerate scenes. By using the additional photometric information, our approach shows drastically improved robustness against geometric degeneracy in environments where all compared baseline approaches fail.

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ICRA Conference 2023 Conference Paper

Credible Online Dynamics Learning for Hybrid UAVs

David Rohr
Nicholas R. J. Lawrance
Olov Andersson
Roland Siegwart

Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics uncertainty quantification. We present a data-efficient, probabilistic semi-parametric dynamics modeling approach that allows for online, filter-based inference. The proposed model leverages prior knowledge using a nominal parametric model, and combines it with residuals in the form of sparse Gaussian processes to account for possibly unmodeled forces and moments. Uncertain nominal and residual parameters are jointly estimated using Bayesian filtering. The resulting model accuracy and the reliability of its predicted uncertainty are analyzed for both a simulated and a real example, where we learn the 6DoF nonlinear dynamics of a tiltwing H-UAV from a few minutes of flight data. Compared to a residual-free nominal model, the proposed semi-parametric approach provides increased model accuracy in relevant parts of the flight envelope and substantially higher credibility overall.

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IROS Conference 2023 Conference Paper

Learning to Open Doors with an Aerial Manipulator

Eugenio Cuniato
Ismail Geles
Weixuan Zhang
Olov Andersson
Marco Tognon
Roland Siegwart

The field of aerial manipulation has seen rapid advances, transitioning from push-and-slide tasks to interaction with articulated objects. The motion trajectory of these complex actions is usually hand-crafted or a result of online optimization methods like Model Predictive Control (MPC) or Model Predictive Path Integral (MPPI) control. However, these methods rely on heuristics or model simplifications to efficiently run on onboard hardware, limiting their robustness, and making them sensitive to disturbances and differences between the real environment and its model. In this work, we propose a Reinforcement Learning (RL) approach to learn reactive motion behaviors for a manipulation task while producing policies that are robust to disturbances and modeling errors. Specifically, we train a policy to perform a door-opening task with an Omnidirectional Micro Aerial Vehicle (OMAV). The policy is trained in a physics simulator and shown in the real world, where it is able to generalize also to door closing tasks never seen in training. We also compare our method against a state-of-the-art MPPI solution in simulation, showing a considerable increase in robustness and speed.

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IROS Conference 2023 Conference Paper

Material-Agnostic Shaping of Granular Materials with Optimal Transport

Nikhilesh Alatur
Olov Andersson
Roland Siegwart
Lionel Ott

From construction materials, such as sand or asphalt, to kitchen ingredients, like rice, sugar, or salt; the world is full of granular materials. Despite impressive progress in robotic manipulation of single objects, granular materials remain a challenge due to difficulties in modelling these highly deformable and inhomogeneous materials, which are governed by dynamics that are hard to capture analytically. We argue that despite the high degrees of freedom and the complex underlying dynamics of granular materials, many practical problems that require manipulating them can be solved by leveraging simple models, informative motion priors, and a fast feedback loop. In this work, we show that computational Optimal Transport (OT) can be leveraged to derive informative, robot-agnostic motion priors for transforming a pile of granular materials from a source into a target distribution and generate robot motion plans with a next-best sweep planner that uses a simple material-agnostic sweep model. We plan sweeps directly on a height map representation of the material distribution and hence avoid a costly particle-level treatment of the problem. We validate our approach with a large set of simulation and hardware experiments that demonstrate several complex shaping tasks, including gathering, separating, and writing letters with different types of granular materials.

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ICRA Conference 2023 Conference Paper

Obstacle avoidance using Raycasting and Riemannian Motion Policies at kHz rates for MAVs

Michael Pantic
Isar Meijer
Rik Bähnemann
Nikhilesh Alatur
Olov Andersson
Cesar Cadena 0001
Roland Siegwart
Lionel Ott

This paper presents a novel method for using Riemannian Motion Policies on volumetric maps, shown in the example of obstacle avoidance for Micro Aerial Vehicles (MAVs), Today, most robotic obstacle avoidance algorithms rely on sampling or optimization-based planners with volumetric maps. However, they are computationally expensive and often have inflexible monolithic architectures. Riemannian Motion Policies are a modular, parallelizable, and efficient navigation alternative but are challenging to use with the widely used voxel-based environment representations. We propose using GPU raycasting and tens of thousands of concurrent policies to provide direct obstacle avoidance using Riemannian Motion Policies in voxelized maps without needing map smoothing or pre-processing. Additionally, we present how the same method can directly plan on LiDAR scans without any intermediate map. We show how this reactive approach compares favorably to traditional planning methods and can evaluate up to 200 million rays per second. We demonstrate the planner successfully on a real MAV for static and dynamic obstacles. The presented planner is made available as an open-source package 1 1 https://github.com/ethz-asl/reactive_avoidance.

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ICRA Conference 2023 Conference Paper

Resilient Terrain Navigation with a 5 DOF Metal Detector Drone

Patrick Pfreundschuh
Rik Bähnemann
Tim Kazik
Thomas Mantel
Roland Siegwart
Olov Andersson

Micro aerial vehicles (MAVs) hold the potential for performing autonomous and contactless land surveys for the detection of landmines and explosive remnants of war (ERW). Metal detectors are the standard detection tool but must be operated close to and parallel to the terrain. A successful combination of MAVs with metal detectors has not been presented yet, as it requires advanced flight capabilities. To this end, we present an autonomous system to survey challenging undulated terrain using a metal detector mounted on a 5 degrees of freedom (DOF) MAV. Based on an online estimate of the terrain, our receding-horizon planner efficiently covers the area, aligning the detector to the surface while considering the kinematic and visibility constraints of the platform. As the survey requires resilient and accurate localization in diverse terrain, we also propose a factor graph-based online fusion of GNSS, IMU, and LiDAR measurements. We validate the robustness of the solution to individual sensor degeneracy by flying under the canopy of trees and over featureless fields. A simulated ablation study shows that the proposed planner reduces coverage duration and improves trajectory smoothness. Real-world flight experiments showcase autonomous mapping of buried metallic objects in undulated and obstructed terrain.

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IROS Conference 2022 Conference Paper

FlowBot: Flow-based Modeling for Robot Navigation

Daniel Dugas
Kuanqi Cai
Olov Andersson
Nicholas R. J. Lawrance
Roland Siegwart
Jen Jen Chung

Autonomous navigation among people is a com-plex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-fluid model of crowd flow for such problems. These have an intuitive physical interpretation and do not require much tuning. We further formalize an observation model to infer flow properties from discrete sensor observations, including support for partial observability, and pair it with a flow-aware planner. We demonstrate the potential of the approach in simulated navigation scenarios. We achieve state of the art results on the CrowdBot navigation benchmark, and also compare favorably against a standard ROS planner on a partially observable environment, demonstrating that the flow-aware planner successfully estimates and plans around counter-flows in the crowd in real time. We conclude that flow-based planning shows great promise for crowded environments that may exhibit such flow-like behavior.

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IROS Conference 2022 Conference Paper

NavDreams: Towards Camera-Only RL Navigation Among Humans

Daniel Dugas
Olov Andersson
Roland Siegwart
Jen Jen Chung

Autonomously navigating a robot in everyday crowded spaces requires solving complex perception and planning challenges. When using only monocular image sensor data as input, classical two-dimensional planning approaches cannot be used. While images present a significant challenge when it comes to perception and planning, they also allow capturing potentially important details, such as complex geometry, body movement, and other visual cues. In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information. We investigate whether the world model concept, which has shown state-of-the-art results for modeling and learning policies in Atari games as well as promising results in 2D LiDAR-based crowd navigation, can also be applied to the camera-based navigation problem. To this end, we create simulated environments where a robot must navigate past static and moving humans without colliding in order to reach its goal. We find that state-of-the-art methods are able to achieve success in solving the navigation problem, and can generate dream-like predictions of future image-sequences which show consistent geometry and moving persons. We are also able to show that policy performance in our high-fidelity sim2real simulation scenario transfers to the real world by testing the policy on a real robot. We make our simulator, models and experiments available at https://github.com/danieldugas/NavDreams.

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IROS Conference 2022 Conference Paper

Reactive Motion Planning for Rope Manipulation and Collision Avoidance using Aerial Robots

Liping Shi
Michael Pantic
Olov Andersson
Marco Tognon
Roland Siegwart
Rune Hylsberg Jacobsen

In this work we address the challenging problem of manipulating a flexible link, like a rope, with an aerial robot. Inspired by spraying tasks in construction and maintenance scenarios, we consider the case in which an autonomous end-effector (e. g. , a spray nozzle moved by a robot or a human operator) is connected to a fixed point by a rope (e. g. , a hose). To avoid collisions between the rope and the environment while the end-effector moves, we propose the use of an aerial robot as a flying companion to properly manipulate the rope away from collisions. The aerial robot is attached to the rope between the end-effector and the fixed point. Assuming no direct control of the end-effector (e. g. , when operated by a human), we design a reactive and fast motion planner for the aerial robot. Grounding on the theory of Forced Geometric Fabrics, we design a motion planner that generates trajectories to drive the aerial robot to follow the end-effector, while manipulating the rope to avoid collisions in cluttered environments. To include the complex behavior of the flexible link, we propose a rope model that estimates its real-time state under forces and position-based interactions, as well as collisions with obstacle surfaces. Finally, we evaluate the system behavior and the motion planner performance in simulations, as well as in real-world experiments on an original spray painting application.

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UAI Conference 2019 Conference Paper

Real-Time Robotic Search using Structural Spatial Point Processes

Olov Andersson
Per Sidén
Johan Dahlin
Patrick Doherty 0001
Mattias Villani

Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial fields for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e. g. geographic or cell phone traffic data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, finding many more injured in the crucial first hours.

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AAAI Conference 2017 Conference Paper

Deep Learning Quadcopter Control via Risk-Aware Active Learning

Olov Andersson
Mariusz Wzorek
Patrick Doherty

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efﬁcacy of the approach on a difﬁcult collision avoidance problem with non-cooperative moving obstacles. Our ﬁndings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

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ICRA Conference 2016 Conference Paper

Model-predictive control with stochastic collision avoidance using Bayesian policy optimization

Olov Andersson
Mariusz Wzorek
Piotr Rudol
Patrick Doherty 0001

Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

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AAAI Conference 2015 Conference Paper

Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization

Olov Andersson
Fredrik Heintz
Patrick Doherty

Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a modelbased reinforcement learning approach for continuous environments with constraints. The approach combines modelbased reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

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