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Marco Camurri

18 accepted papers

2026

Smooth Human-Robot Shared Control for Autonomous Orchard Monitoring with UGVs (I)

Cheikh Melainine El Bou, Michele Focchi, Michael Chang, Marco Camurri, Karl Dietrich von Ellenrieder
ICRA 2026poster

Precision agriculture offers the opportunity to auto- mate routine or difficult tasks in orchards and vineyards, such as spraying or inspection, with Unmanned Ground Vehicles (UGV). In this context, human operators should be kept in the closed-loop control of the robot for safety and reliability. Th…

Cited by 0Scholar ↗
2023

Deep IMU Bias Inference for Robust Visual-Inertial Odometry With Factor Graphs

Russell Buchanan, Varun Agrawal, Marco Camurri, Frank Dellaert, Maurice F. Fallon
RA-L 2023

Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of addit

Cited by 48Source ↗Scholar ↗
2023

Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station

Jonas Beuchert, Marco Camurri, Maurice Fallon
ICRA 2023poster

Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a r…

Cited by 28Source ↗code ↗Scholar ↗
2023

Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping

Lintong Zhang, Michael Helmberger, Lanke Frank Tarimo Fu, David Wisth, Marco Camurri, Davide Scaramuzza +1
RA-L 2023

Simultaneous Localization and Mapping (SLAM) is being deployed in real-world applications, however many state-of-the-art solutions still struggle in many common scenarios. A key necessity in progressing SLAM research is the availability of high-quality datasets and fair and transparent benchmarking.

Cited by 79Source ↗Scholar ↗
2022

Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry

Lintong Zhang, David Wisth, Marco Camurri, Maurice F. Fallon
RA-L 2022

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with

Cited by 31Source ↗Scholar ↗
2022

Unsupervised Learning of Terrain Representations for Haptic Monte Carlo Localization

Mikołaj Łysakowski, Michał R. Nowicki, Russell Buchanan, Marco Camurri, Maurice Fallon, Krzysztof Walas
ICRA 2022poster

Haptic sensing has recently been used effectively for legged robot localization in extreme scenarios where cam-eras and LiDAR might fail, such as dusty mines and foggy sewers. However, existing haptic sensing mainly relies on supervised classification, with training and evaluation executed over expl…

Cited by 5Source ↗Scholar ↗
2021

Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks

Yiduo Wang, Nils Funk, Milad Ramezani, Sotiris Papatheodorou, Marija Popović, Marco Camurri +2
ICRA 2021poster

We present an efficient, elastic 3D LiDAR reconstruction framework which can reconstruct up to maximum Li-DAR ranges (60 m) at multiple frames per second, thus enabling robot exploration in large-scale environments. Our approach only requires a CPU. We focus on three main challenges of large-scale r…

Cited by 25Source ↗Scholar ↗
2021

Learning Camera Performance Models for Active Multi-Camera Visual Teach and Repeat

Matias Mattamala, Milad Ramezani, Marco Camurri, Maurice Fallon
ICRA 2021poster

In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera Performance Models (CPM) which evaluate the cam…

Cited by 14Source ↗Scholar ↗
2021

Learning Inertial Odometry for Dynamic Legged Robot State Estimation

Russell Buchanan, Marco Camurri, Frank Dellaert, Maurice Fallon
CoRL 2021poster

This paper introduces a novel proprioceptive state estimator for legged robots based on a learned displacement measurement from IMU data. Recent research in pedestrian tracking has shown that motion can be inferred from inertial data using convolutional neural networks. A learned inertial displaceme…

Cited by 41Source ↗Scholar ↗
2021

Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry

David Wisth, Marco Camurri, Sandipan Das, Maurice F. Fallon
RA-L 2021

We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to ext

Cited by 114Source ↗Scholar ↗
2020

Haptic Sequential Monte Carlo Localization for Quadrupedal Locomotion in Vision-Denied Scenarios

Russell Buchanan, Marco Camurri, Maurice Fallon
IROS 2020poster

Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localizati…

Cited by 5Source ↗Scholar ↗
2020

Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry

David Wisth, Marco Camurri, Maurice Fallon
ICRA 2020poster

In this paper, we present a novel factor graph formulation to estimate the pose and velocity of a quadruped robot on slippery and deformable terrain. The factor graph introduces a preintegrated velocity factor that incorporates velocity inputs from leg odometry and also estimates related biases. Fro…

Cited by 38Source ↗Scholar ↗
2020

The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth

Milad Ramezani, Yiduo Wang, Marco Camurri, David Wisth, Matias Mattamala, Maurice Fallon
IROS 2020poster

In this paper, we present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km around New College, Oxford as well as a series of supplementary datasets with much more aggressive motion and lighting contrast. Th…

Cited by 238Source ↗Scholar ↗
2019

Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

Octavio Antonio Villarreal-Magaña, Victor Barasuol, Marco Camurri, Luca Franceschi, Michele Focchi, Massimiliano Pontil +2
RA-L 2019

Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees r

Cited by 81Source ↗Scholar ↗
2017

Heterogeneous Sensor Fusion for Accurate State Estimation of Dynamic Legged Robots

Simona Nobili, Marco Camurri, Victor Barasuol, Michele Focchi, Darwin Caldwell, Claudio Semini +1
RSS 2017poster

In this paper we present a system for the state estimation of a dynamically walking and trotting quadruped. The approach fuses four heterogeneous sensor sources (inertial, kinematic, stereo vision and LIDAR) to maintain an accurate and consistent estimate of the robot's base link velocity and positi…

Cited by 84Source ↗PDF ↗Scholar ↗
2017

Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots

Marco Camurri, Maurice F. Fallon, Stéphane Bazeille, Andreea Radulescu, Victor Barasuol, Darwin G. Caldwell +1
RA-L 2017

Reliable state estimation is crucial for stable planning and control of legged locomotion. A fundamental component of a state estimator in legged platforms is Leg Odometry, which only requires information about kinematics and contacts. Many legged robots use dedicated sensors on each foot to detect

Cited by 114Source ↗Scholar ↗
2015

Reactive trotting with foot placement corrections through visual pattern classification

Victor Barasuol, Marco Camurri, Stephane Bazeille, Darwin G. Caldwell, Claudio Semini
IROS 2015poster

Agile robot locomotion on rough terrain is highly dependent on the ability to perceive the environment. In this paper, we show how the interaction between a reactive control framework and an online mapping system can significantly improve the trotting performance on irregular terrain. In particular,…

Cited by 20Source ↗Scholar ↗