Visual and Inertial Odometry and SLAM

Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning

Considering visual localization accuracy at the planning time gives preference to robot motion that can be better localized and is of benefit to vision-based navigation. To integrate the knowledge about localization accuracy in planning, a common approach is to compute the Fisher information of the pose estimation process from a set of sparse landmarks. However, this approach scales linearly with the number of landmarks and introduces redundant computation. To overcome these drawbacks, we propose the first dedicated map for evaluating the Fisher information of 6 degree-of-freedom visual localization for perception-aware planning. We separate and precompute the rotational invariant component from the Fisher information and store it in a voxel grid, namely the Fisher information field. The Fisher information for arbitrary poses can then be computed from the field in constant time. Experimental results show that the proposed Fisher information field can be applied to different planning algorithms and is at least 10 times faster than using the point cloud. Moreover, the proposed map is differentiable, resulting in better performance in trajectory optimization algorithms.

References

arXiv20_Zhang_FIF

Z. Zhang, D. Scaramuzza

Fisher Information Field: an Efficient and Differentiable Map for Perception-aware Planning

arXiv preprint, 2020.

PDF Video Code


ICRA19_Zhang

Z. Zhang, D. Scaramuzza

Beyond Point Clouds: Fisher Information Field for Active Visual Localization

IEEE International Conference on Robotics and Automation, 2019.

PDF Video Code


Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry


Motivated by the goal of achieving robust, drift-free pose estimation in long-term autonomous navigation, in this work we propose a methodology to fuse global positional information with visual and inertial measurements in a tightly-coupled nonlinear-optimization based estimator. Differently from previous works, which are loosely-coupled, the use of a tightly-coupled approach allows exploiting the correlations amongst all the measurements. A sliding window of the most recent system states is estimated by minimizing a cost function that includes visual re-projection errors, relative inertial errors, and global positional residuals. We use IMU preintegration to formulate the inertial residuals and leverage the outcome of such algorithm to efficiently compute the global position residuals. The experimental results show that the proposed method achieves accurate and globally consistent estimates, with negligible increase of the optimization computational cost. Our method consistently outperforms the loosely-coupled fusion approach. The mean position error is reduced up to 50% with respect to the loosely-coupled approach in outdoor Unmanned Aerial Vehicle (UAV) flights, where the global position information is given by noisy GPS measurements. To the best of our knowledge, this is the first work where global positional measurements are tightly fused in an optimization-based visual-inertial odometry algorithm, leveraging the IMU preintegration method to define the global positional factors.


References

IROS20_Cioffi

Giovanni Cioffi, Davide Scaramuzza

Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, 2020.

PDF YouTube


Reference Pose Generation for Visual Localization via Learned Features and View Synthesis


Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality. High quality datasets with accurate 6 Degree-of-Freedom (DoF) reference poses are the foundation for benchmarking and improving existing methods. Traditionally, reference poses have been obtained via Structure-from-Motion (SfM). However, SfM itself relies on local features which are prone to fail when images were taken under different conditions, e.g., day/night changes. At the same time, manually annotating feature correspondences is not scalable and potentially inaccurate. In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features. Given an initial pose estimate, our approach iteratively refines the pose based on feature matches against a rendering of the model from the current pose estimate. We significantly improve the nighttime reference poses of the popular Aachen Day-Night dataset, showing that state-of-the-art visual localization methods perform better (up to 47%) than predicted by the original reference poses. We extend the dataset with new nighttime test images, provide uncertainty estimates for our new reference poses, and introduce a new evaluation criterion. We will make our reference poses and our framework publicly available upon publication.


References

Arxiv20_Zhang

Zichao Zhang, Torsten Sattler, Davide Scaramuzza

Reference Pose Generation for Visual Localization via Learned Features
and View Synthesis

ArXiv Preprint, 2020.

PDF


GPU-Accelerated Frontend for High-Speed VIO


The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms in the field of visual-inertial odometry (VIO). While most steps of a VIO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency. Our work first revisits the problem of non-maxima suppression for feature detection specifically on GPUs, and proposes a solution that selects local response maxima, imposes spatial feature distribution, and extracts features simultaneously. Our second contribution introduces an enhanced FAST feature detector that applies the aforementioned non-maxima suppression method. Finally, we compare our method to other state-of-the-art CPU and GPU implementations, where we always outperform all of them in feature tracking and detection, resulting in over 1000fps throughput on an embedded Jetson TX2 platform. Additionally, we demonstrate our work integrated in a VIO pipeline achieving a metric state estimation at ~200fps.


References

Arxiv20_Nagy

Balazs Nagy, Philipp Foehn, D. Scaramuzza

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, 2020.

PDF Code YouTube


Voxel Map for Visual SLAM


In modern visual SLAM systems, it is a standard practice to retrieve potential candidate map points from overlapping keyframes for further feature matching or direct tracking. In this work, we argue that keyframes are not the optimal choice for this task, due to several inherent limitations, such as weak geometric reasoning and poor scalability. We propose a voxel-map representation to efficiently retrieve map points for visual SLAM. In particular, we organize the map points in a regular voxel grid. Visible points from a camera pose are queried by sampling the camera frustum in a raycasting manner, which can be done in constant time using an efficient voxel hashing method. Compared with keyframes, the retrieved points using our method are geometrically guaranteed to fall in the camera field-of-view, and occluded points can be identified and removed to a certain extend. This method also naturally scales up to large scenes and complicated multi-camera configurations. Experimental results show that our voxel map representation is as efficient as a keyframe map with 5 keyframes and provides significantly higher localization accuracy (average 46% improvement in RMSE) on the EuRoC dataset. The proposed voxel-map representation is a general approach to a fundamental functionality in visual SLAM and widely applicable

References

ICRA20_Muglikar

M. Muglikar, Z. Zhang, D. Scaramuzza

Voxel Map for Visual SLAM

IEEE International Conference on Robotics and Automation, 2020.

PDF ICRA2020 Pitch Video


Redesigning SLAM for Arbitrary Multi-Camera Systems

Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To this end, we revisit several common building blocks in visual SLAM. In particular, we propose an adaptive initialization scheme, a sensor-agnostic, information-theoretic keyframe selection algorithm, and a scalable voxel-based map. These techniques make little assumption about the actual camera setups and prefer theoretically grounded methods over heuristics. We adapt a state-of-the-art visual-inertial odometry with these modifications, and experimental results show that the modified pipeline can adapt to a wide range of camera setups (e.g., 2 to 6 cameras in one experiment) without the need of sensor-specific modifications or tuning.

References

ICRA20_Kuo

J. Kuo, M. Muglikar, Z. Zhang, D. Scaramuzza

Redesigning SLAM for Arbitrary Multi-Camera Systems

IEEE International Conference on Robotics and Automation, 2020.

PDF Video ICRA2020 Pitch Video


Smart Interest Points


Detecting interest points is a key component of vision-based estimation algorithms, such as visual odometry or visual SLAM. In the context of distributed visual SLAM, we have encountered the need to minimize the amount of data that is sent between robots, which, for relative pose estimation, translates into the need to find a minimum set of interest points that is sufficiently reliably detected between viewpoints to ensure relative pose estimation. We have decided to solve this problem at a fundamental level, that is, at the point detector, using machine learning.

In SIPS, we introduce the succinctness metric, which allows to quantify performance of interest point detectors with respect to this goal. At the same time, we propose an unsupervised training method for CNN interest point detectors which requires no labels - only uncalibrated image sequences. The proposed method is able to establish relative poses with a minimum of extracted interest points. However, descriptors still need to be extracted and transmitted to establish these poses.

This problem is addressed in IMIPs, where we propose the first feature matching pipeline that works by implicit matching, without the need of descriptors. In IMIPs, the detector CNN has multiple output channels, and each channel generates a single interest point. Between viewpoints, interest points obtained from the same channel are considered implicitly matched. This allows matching points with as little as 3 bytes per point - the point coordinates in an up to 4096 x 4096 image.


References

IMIPs

T. Cieslewski, M. Bloesch, D. Scaramuzza

Matching Features without Descriptors:
Implicitly Matched Interest Points

British Machine Vision Conference (BMVC), Cardiff, 2019.

PDF Poster Code and Data


SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

T. Cieslewski, K. G. Derpanis, D. Scaramuzza

SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning

IEEE International Conference on 3D Vision (3DV), 2019.

PDF Poster YouTube Code and Data


Visual-Inertial Odometry of Aerial Robotics

encyclopedia_vio
Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of an agent (e.g., an aerial robot) by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it. VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation. Since both cameras and IMUs are very cheap, these sensor types are ubiquitous in all today's aerial robots.

References

encyclopedia19_scaramuzza

D. Scaramuzza, Z. Zhang

Visual-Inertial Odometry of Aerial Robots

Encyclopedia of Robotics, Springer, 2019

PDF


Probabilistic, Continuous-Time Trajectory Evaluation for SLAM

Trajectory Evaluation
Despite the existence of different error metrics for trajectory evaluation in SLAM, their theoretical justifications and connections are rarely studied, and few methods handle temporal association properly. In this work, we propose to formulate the trajectory evaluation problem in a probabilistic, continuous-time framework. By modeling the groundtruth as random variables, the concepts of absolute and relative error are generalized to be likelihood. Moreover, the groundtruth is represented as a piecewise Gaussian Process in continuous-time. Within this framework, we are able to establish theoretical connections between relative and absolute error metrics and handle temporal association in a principled manner.

References

WICRA19_Zhang

Z. Zhang, D. Scaramuzza

Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach

ICRA19 Workshop on Dataset Generation and Benchmarking of SLAM Algorithms for Robotics and VR/AR

Best Paper Award!

PDF


Visual Inertial Model-based Odometry and Force Estimation

In recent years, many approaches to Visual Inertial Odometry (VIO) have become available. However, they neither exploit the robot's dynamics and known actuation inputs, nor differentiate between desired motion due to actuation and unwanted perturbation due to external force. For many robotic applications, it is often essential to sense the external force acting on the system due to, for example, interactions, contacts, and disturbances. Adding a motion constraint to an estimator leads to a discrepancy between the model-predicted motion and the actual motion. Our approach exploits this discrepancy and resolves it by simultaneously estimating the motion and the external force. We propose a relative motion constraint combining the robot's dynamics and the external force in a preintegrated residual, resulting in a tightly-coupled, sliding-window estimator exploiting all correlations among all variables. We implement our Visual Inertial Model-based Odometry (VIMO) system into a state-of-the-art VIO approach and evaluate it against the original pipeline without motion constraints on both simulated and real-world data. The results show that our approach increases the accuracy of the estimator up to 29\% compared to the original VIO, and provides external force estimates at no extra computational cost. To the best of our knowledge, this is the first approach exploiting model dynamics by jointly estimating motion and external force.

References

ICRA19_Zhang

B. Nisar, P. Foehn, D. Falanga, D. Scaramuzza

VIMO: Simultaneous Visual Inertial Model-based Odometry and Force Estimation

Robotics: Science and Systems (RSS), Freiburg, 2019

PDF Video Code


Fisher Information Field for Active Visual Localization

For mobile robots to localize robustly, actively considering the perception requirement at the planning stage is essential. In this paper, we propose a novel representation for active visual localization. By formulating the Fisher information and sensor visibility carefully, we are able to summarize the localization information into a discrete grid, namely the Fisher information field. The information for arbitrary poses can then be computed from the field in constant time, without the need of costly iterating all the 3D landmarks. Experimental results on simulated and real-world data show the great potential of our method in efficient active localization and perception- aware planning. To benefit related research, we release our implementation of the information field to the public.

References

ICRA19_Zhang

Z. Zhang, D. Scaramuzza

Beyond Point Clouds: Fisher Information Field for Active Visual Localization

IEEE International Conference on Robotics and Automation, 2019.

PDF Video Code


A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry

Trajectory Evaluation
In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to determine the transformation type to use in trajectory alignment based on the specific sensing modality (i.e., monocular, stereo and visual-inertial). Second, we describe commonly used error metrics (i.e., the absolute trajectory error and the relative error) and their strengths and weaknesses. To make the methodology presented for VO/VIO applicable to other setups, we also generalize our formulation to any given sensing modality. To facilitate the reproducibility of related research, we publicly release our implementation of the methods described in this tutorial.

References

Trajectory Evaluation

Z. Zhang, D. Scaramuzza

A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, 2018.

PDF PPT VO/VIO Evaluation Toolbox


On the Comparison of Gauge Freedom Handling in Optimization-based Visual-Inertial State Estimation

Gauge Comparison
It is well known that visual-inertial state estimation is possible up to a four degrees-of-freedom (DoF) transformation (rotation around gravity and translation), and the extra DoFs ("gauge freedom") have to be handled properly. While different approaches for handling the gauge freedom have been used in practice, no previous study has been carried out to systematically analyze their differences. In this paper, we present the first comparative analysis of different methods for handling the gauge freedom in optimization-based visual-inertial state estimation. We experimentally compare three commonly used approaches: fixing the unobservable states to some given values, setting a prior on such states, or letting the states evolve freely during optimization. Specifically, we show that (i) the accuracy and computational time of the three methods are similar, with the free gauge approach being slightly faster; (ii) the covariance estimation from the free gauge approach appears dramatically different, but is actually tightly related to the other approaches. Our findings are validated both in simulation and on real-world datasets and can be useful for designing optimization-based visual-inertial state estimation algorithms.

References

Gauge Comparison

Z. Zhang, G, Gallego, D. Scaramuzza

On the Comparison of Gauge Freedom Handling in Optimization-based Visual-Inertial State Estimation

IEEE Robotics and Automation Letters (RA-L), 2018.

PDF PPT Code


Visual-Inertial Odometry Benchmarking

Flying robots require a combination of accuracy and low latency in their state estimation in order to achieve stable and robust flight. However, due to the power and payload constraints of aerial platforms, state estimation algorithms must provide these qualities under the computational constraints of embedded hardware. Cameras and inertial measurement units (IMUs) satisfy these power and payload constraints, so visual-inertial odometry (VIO) algorithms are popular choices for state estimation in these scenarios, in addition to their ability to operate without external localization from motion capture or global positioning systems. It is not clear from existing results in the literature, however, which VIO algorithms perform well under the accuracy, latency, and computational constraints of a flying robot with onboard state estimation. This paper evaluates an array of publicly-available VIO pipelines (MSCKF, OKVIS, ROVIO, VINS-Mono, SVO+MSF, and SVO+GTSAM) on different hardware configurations, including several single-board computer systems that are typically found on flying robots. The evaluation considers the pose estimation accuracy, per-frame processing time, and CPU and memory load while processing the EuRoC datasets, which contain six degree of freedom (6DoF) trajectories typical of flying robots. We present our complete results as a benchmark for the research community.

References

A Benchmark Comparison of Monocular VIO Algorithms for Flying Robots

J. Delmerico, D. Scaramuzza

A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots

IEEE International Conference on Robotics and Automation (ICRA), 2018.

PDF Video PPT


Active Exposure Control for Robust Visual Odometry in High Dynamic Range (HDR) Environments

In this paper, we propose an active exposure control method to improve the robustness of visual odometry in HDR (high dynamic range) environments. Our method evaluates the proper exposure time by maximizing a robust gradient-based image quality metric. The optimization is achieved by exploiting the photometric response function of the camera. Our exposure control method is evaluated in different real world environments and outperforms both the built-in auto-exposure function of the camera and a fixed exposure time. To validate the benefit of our approach, we test different state-of-the-art visual odometry pipelines (namely, ORB-SLAM2, DSO, and SVO 2.0) and demonstrate significant improved performance using our exposure control method in very challenging HDR environments. Datasets and code will be released soon!

References

ICRA17_Zhang

Z. Zhang, C. Forster, D. Scaramuzza

Active Exposure Control for Robust Visual Odometry in HDR Environments

IEEE International Conference on Robotics and Automation (ICRA), 2017.

PDF YouTube


IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. However, the improvement comes at the cost of increased computational complexity. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes. The preintegration allows us to accurately summarize hundreds of inertial measurements into a single relative motion constraint. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation. The measurements are integrated in a local frame, which eliminates the need to repeat the integration when the linearization point changes while leaving the opportunity for belated bias corrections. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated in a visual-inertial pipeline under the unifying framework of factor graphs. This enables the use of a structureless model for visual measurements, further accelerating the computation. The third contribution is an extensive evaluation of our monocular VIN pipeline: experimental results confirm that our system is very fast and demonstrates superior accuracy with respect to competitive state-of-the-art filtering and optimization algorithms, including off-the-shelf systems such as Google Tango.

References

RSS15_Forster

C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza

On-Manifold Preintegration for Real-Time Visual-Inertial Odometry

IEEE Transactions on Robotics, in press, 2016.

PDF YouTube


RSS2015_Forster

C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza

IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

Robotics: Science and Systems (RSS), Rome, 2015.

Best Paper Award Finalist! Oral Presentation: Acceptance Rate 4%

PDF Supplementary material YouTube


SVO: Fast Semi-Direct Monocular Visual Odometry


We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and faster than current state-of-the-art methods. The semi-direct approach eliminates the need of costly feature extraction and robust matching techniques for motion estimation. Our algorithm operates directly on pixel intensities, which results in subpixel precision at high frame-rates. A probabilistic mapping method that explicitly models outlier measurements is used to estimate 3D points, which results in fewer outliers and more reliable points. Precise and high frame-rate motion estimation brings increased robustness in scenes of little, repetitive, and high-frequency texture. The algorithm is applied to micro-aerial-vehicle stateestimation in GPS-denied environments and runs at 55 frames per second on the onboard embedded computer and at more than 300 frames per second on a consumer laptop.


This video shows results from a modification of the SVO algorithm that generalizes to a set of rigidly attached (not necessarily overlapping) cameras. Simultaneously, we run a CPU implementation of the REMODE algorithm on the front, left, and right camera. Everything runs in real-time on a laptop computer. Parking garage dataset courtesy of NVIDIA.

References

TRO17_Forster-SVO

Christian Forster, Zichao Zhang, Michael Gassner, Manuel Werlberger, Davide Scaramuzza

SVO: Semi-Direct Visual Odometry for Monocular and Multi-Camera Systems

IEEE Transactions on Robotics, Vol. 33, Issue 2, pages 249-265, Apr. 2017.

Includes comparison against ORB-SLAM, LSD-SLAM, and DSO and comparison among Dense, Semi-dense, and Sparse Direct Image Alignment.

PDF YouTube Binaries Download


ICRA2014_Forster

C. Forster, M. Pizzoli, D. Scaramuzza

SVO: Fast Semi-Direct Monocular Visual Odometry

IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014.

PDF YouTube Software SVO 2.0 Binaries Download


ICRA2014_Pizzoli

M. Pizzoli, C. Forster, D. Scaramuzza

REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time

IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014.

PDF YouTube


1-point RANSAC

Given a car equipped with an omnidirectional camera, the motion of the vehicle can be purely recovered from salient features tracked over time. We propose the 1-Point RANSAC algorithm which runs at 800 Hz on a normal laptop. To our knowledge, this is the most efficient visual odometry algorithm.



This video shows the estimation of the vehicle motion from image features. The video demonstrate the approach described in our paper which uses 1-point RANSAC algorithm to remove the outliers. Except for the features extraction process, the outlier removal and the motion estimation steps take less than 1 ms on a normal laptop computer.

References

D. Scaramuzza and F. Fraundorfer. Visual Odometry: Part I - The First 30 Years and Fundamentals. IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011. [ PDF ]
F. Fraundorfer and D. Scaramuzza. Visual odometry: Part II - Matching, robustness, optimization, and applications. IEEE Robotics and Automation Magazine, Volume 19, issue 2, 2012. [ PDF ]
D. Scaramuzza. 1-Point-RANSAC Structure from Motion for Vehicle-Mounted Cameras by Exploiting Non-holonomic Constraints. International Journal of Computer Vision, Volume 95, Issue 1, 2011. [ PDF ]
D. Scaramuzza. Performance Evaluation of 1-Point-RANSAC Visual Odometry. Journal of Field Robotics, Volume 28, issue 5, 2011. PDF ]
D. Scaramuzza, A. Censi, K. Daniilidis. Exploiting Motion Priors in Visual Odometry for Vehicle-Mounted Cameras with Non-holonomic Constraints. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, September, 2011. [ PDF ]
L. Kneip, D. Scaramuzza, R. Siegwart. A Novel Parameterization of the Perspective-Three-Point Problem for a Direct Computation of Absolute Camera Position and Orientation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, USA, 2011. [ PDF ] [C/C++ code]
L. Kneip, A. Martinelli, S. Weiss, D. Scaramuzza, R. Siegwart. A Closed-Form Solution for Absolute Scale Velocity Determination Combining Inertial Measurements and a Single Feature Correspondence. IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, 2011. [ PDF ]
D. Scaramuzza, F. Fraundorfer, and M. Pollefeys. Closing the Loop in Appearance-Guided Omnidirectional Visual Odometry by Using Vocabulary Trees. Robotics and Autonomous System Journal (Elsevier), Volume 58, issue 6, June, 2010. [ PDF ]
L. Kneip, D. Scaramuzza, R. Siegwart. On the Initialization of Statistical Optimum Filters with Application to Motion Estimation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, October, 2010. [ PDF ]
F. Fraundorfer, D. Scaramuzza, M. Pollefeys. A Constricted Bundle Adjustment Parameterization for Relative Scale Estimation in Visual Odometry. IEEE International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, May, 2010. [ PDF ]
D. Scaramuzza, L. Spinello, R. Triebel, R., Siegwart. Key Technologies for Intelligent and Safer Cars from Motion Estimation to Predictive Motion Planning. IEEE International Conference on Industrial Electronics, Bari, Italy, July, 2010. [ PDF ]
D. Sabatta, D. Scaramuzza, R. Siegwart. Improved Appearance-Based Matching in Similar and Dynamic Environments Using a Vocabulary Tree. IEEE International Conference on Robotics and Automation (ICRA 2010), Anchorage, Alaska, May, 2010. [ PDF ]
D. Scaramuzza, F. Fraundorfer, M. Pollefeys, R. Siegwart. Absolute Scale in Structure from Motion from a Single Vehicle Mounted Camera by Exploiting Nonholonomic Constraints. IEEE International Conference on Computer Vision (ICCV 2009), Kyoto, September-October, 2009. [ PDF ]
D. Scaramuzza, F. Fraundorfer, R. Siegwart. Real-Time Monocular Visual Odometry for On-Road Vehicles with 1-Point RANSAC. IEEE International Conference on Robotics and Automation (ICRA 2009), Kobe, Japan, May, 2009. [ PDF ]
D. Scaramuzza, R. Siegwart. Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles. IEEE Transactions on Robotics, Volume 24, issue 5, October 2008. [ PDF ]

Robot Localization Using Soft Object Detection

Most of the work done in localization, mapping, and navigation for both ground and aerial vehicles has been done by means of point landmarks or occupancy grids, using vision or laser range finders. However, to make these robots one day able to cooperate with humans in complex scenarios, we need to build semantic maps of the environment. In this work we address map-based localization using "soft" object detection. Soft object detection differs from "hard" object detection in that we do not extract an "affirmative/negative" response about the presence of the object but rather we compute, for each pixel in the current frame, the probability that the object under consideration is there. This gives raise to many false positive (see the multiple peaks in the object "heat-map") that are disambiguated during motion by the particle filter.

References

R. Anati, D. Scaramuzza, K. Derpanis, K. Daniilidis.

Robot Localization Using Soft Object Detection

IEEE International Conference on Robotics and Automation (ICRA), St. Paul, 2012.

PDF