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Documentation of MRPT by J.L.Blanco & contributors is licensed under a Creative Commons License.
paper
MRPT papers
What's this?
This is a repository of scientific papers whose experimental results were carried out using the MRPT library and tools. Along with each paper, you'll also find the corresponding datasets, the source code used for the experiments and/or instructions/scripts to reproduce the results.
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See also: A direct search in Google Scholar for publications mentioning MRPT.
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6 DoF SLAM using a ToF Camera: The challenge of a continuously growing number of landmarks
"6 DoF SLAM using a ToF Camera: The challenge of a continuously growing number of landmarks", Siegfried Hochdorfer, Christian Schlegel, IROS 2010 - (Link).
Authors are with the Collaborative Center for Applied Research (http://www.zafh-servicerobotik.de/), Ulm, Germany.
Online video with results from this paper:
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- 1128 reads
TechRep: A tutorial on SE(3) transformation parameterizations and on-manifold optimization
"A tutorial on SE(3) transformation parameterizations and on-manifold optimization", J.L. Blanco, Technical report, 2010. (PDF, Bibtex). Updated: 12/SEP/2010.
Abstract: An arbitrary rigid transformation in SE(3) can be separated into two parts, namely, a translation and a rigid rotation. This technical report reviews, under a unifying viewpoint, three common alternatives to representing the rotation part: sets of three (yaw-pitch-roll) Euler angles, orthogonal rotation matrices from SO(3) and quaternions. It will be described: (i) the equivalence between these representations and the formulas for transforming one to each other (in all cases considering the translational and rotational parts as a whole), (ii) how to compose poses in each representation and (iii) how the uncertainty of the poses (when modeled as Gaussian distributions) is affected by these transformations and compositions. Some brief notes are also given about the Jacobians required to implement least-squares optimization on manifolds, an very promising approach in recent SLAM literature. The text reflects which MRPT C++ library functions implement each of the described algorithms. All the implementations have been thoroughly validated by means of unit testing.
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- 1309 reads
Optimal Filtering for Non-Parametric Observation Models: Applications to Localization and SLAM
"Optimal Filtering for Non-Parametric Observation Models: Applications to Localization and SLAM", J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal, The International Journal of Robotics Research (IJRR), (In press), DOI: 10.1177/0278364910364165 2010.
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- 420 reads
Paper: Malaga dataset 2009 with 6D ground truth
A Collection of Outdoor Robotic Datasets with centimeter-accuracy Ground Truth, Jose-Luis Blanco, Francisco-Angel Moreno, Javier Gonzalez, Autonomous Robots , 2009 - (Draft PDF) - (Official PDF )
Abstract: The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth renders specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of Visual SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a test-bed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download
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- 1849 reads
Paper:J.L. Blanco's Phd Thesis
Downloads: PDF (12.6Mb) - Slides (17.5 Mb) - Slides+videos (196 Mb)
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Paper:RO-SLAM with SOG
Abstract: This work addresses Range-Only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between each beacon distribution within a Rao-Blackwellized Particle Filter (RBPF) for maintaining independent Sum of Gaussians (SOGs) for each map element. It is shown then that a proper probabilistic observation model can be derived for online operation with no need for delayed initializations. We provide a rigorous statistical comparison of this proposal with previous work of the authors where a Monte-Carlo approximation was employed instead for the conditional densities. As verified experimentally, this new proposal represents a significant improvement in accuracy, computation time, and robustness against outliers.
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- 305 reads
Paper:Subjective Local Maps for Hybrid Metric-Topological SLAM
Abstract: Hybrid maps where local metric sub-maps are kept in the nodes of a graph-based topological structure are gaining relevance as the focus of robot Simultaneous Localization and Mapping (SLAM) shifts towards spatial scalability and long-term operation. In this paper we examine the applicability of spectral graph partitioning techniques to automatically generate metric sub-maps by establishing groups in the sequence of observations gathered by the robot. One of the main aims of this work is to provide a probabilistically grounded interpretation of such a partitioning technique in the context of generating these local maps. We also discuss how to apply it to different kinds of sensory data (stereo images and laser range scans) and how to consider them simultaneously. An important feature of our approach is that it implicitly takes into account the intrinsic characteristics of the sensors, such as the sensor field of view, to perform the partitioning instead of applying heuristics supplied by a human as in other works, and thus the robot builds "subjective" local maps. The ideas presented here are supported by experimental results from a real mobile robot as well as simulations for statistical analysis. We discuss the effects of considering different combinations of sensors in the resulting clustering of the environment.
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- 711 reads
Paper:UWB Particle Filter Localization
Abstract: This article addresses the problem of mobile robot localization using Ultra-Wide-Band (UWB) range measurements. UWB is a radio technology widely used for communications that recently is receiving increasing attention also for positioning applications. In these cases, the position of a mobile transceiver is determined from the distances to a set of fixed, well-localized beacons. Though this is a well-known problem in the scientific literature (the trilateration problem), the peculiarities of UWB range measurements (basically, distance errors and multipath effects) demand a different treatment to other similar solutions as for example those based on laser. This work presents a thorough experimental characterization of UWB ranges within a variety of environments and situations. From these experiments we derive a probabilistic model which is then employed by a particle filter to combine different readings from real UWB beacons as well as the vehicle odometry. To account for the possible offset error due to multipath effects, the state tracked by the particle filter includes the offset of each beacon in addition to the planar robot pose (x,y,φ), both estimated sequentially. We show real experimental results for a robot moving in indoor scenarios covered by three UWB beacons that validate our proposal .
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- 1244 reads
Paper:Stereo Vision Models for RBPF SLAM
Abstract:This work addresses the SLAM problem for stereo vision systems under the unified formulation of particle filter methods. In contrast to most existing approaches to visual SLAM, the present method does not rely on restrictive smooth camera motion models, but on computing incremental 6D pose differences from the image flow through a probabilistic visual odometry method. Moreover, our observation model, which considers both the 3D positions and the SIFT descriptors of the landmarks, avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all the possible associations. We have experimentally validated our research with two experiments in indoor scenarios.
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