Category Archives: Mobile Robot Mapping

Qualitative maps for mobile robots

October 30, 2017 08:59 ,

Jennifer Padgett, Mark Campbell, Probabilistic qualitative mapping for robots, Robotics and Autonomous Systems, Volume 98, 2017, Pages 292-306, DOI: 10.1016/j.robot.2017.09.013.

A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments.

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Taking into account explicitly the dynamics of the environment, and in particular the diverse frequencies of changes, for mobile robot mapping

August 10, 2017 09:56 ,

T. Krajník, J. P. Fentanes, J. M. Santos and T. Duckett, FreMEn: Frequency Map Enhancement for Long-Term Mobile Robot Autonomy in Changing Environments, IEEE Transactions on Robotics, vol. 33, no. 4, pp. 964-977, DOI: 10.1109/TRO.2017.2665664.

We present a new approach to long-term mobile robot mapping in dynamic indoor environments. Unlike traditional world models that are tailored to represent static scenes, our approach explicitly models environmental dynamics. We assume that some of the hidden processes that influence the dynamic environment states are periodic and model the uncertainty of the estimated state variables by their frequency spectra. The spectral model can represent arbitrary timescales of environment dynamics with low memory requirements. Transformation of the spectral model to the time domain allows for the prediction of the future environment states, which improves the robot’s long-term performance in changing environments. Experiments performed over time periods of months to years demonstrate that the approach can efficiently represent large numbers of observations and reliably predict future environment states. The experiments indicate that the model’s predictive capabilities improve mobile robot localization and navigation in changing environments.

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Interesting review of approaches to visually detect loop closings in robotics, and a novel, very efficient method that is independent on the image representation and based on not using the typical l2 norm (least squares), which leads to dense optimization problems

June 30, 2017 11:10 ,

Yasir Latif, Guoquan Huang, John Leonard, José Neira, Sparse optimization for robust and efficient loop closing, Robotics and Autonomous Systems, Volume 93, July 2017, Pages 13-26, ISSN 0921-8890,DOI: 10.1016/j.robot.2017.03.016.

It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation. A key insight explored in this work is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous images. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex
ℓ 1 -minimization problem. By leveraging fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation with no restriction imposed on how images should be represented, while requiring only that the representations are “close” to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using real-world datasets.

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Spatio-temporal maps for mobile robots: taking into account time into the map

June 22, 2017 09:14 ,

João Machado Santos, Tomáš Krajník, Tom Duckett, Spatio-temporal exploration strategies for long-term autonomy of mobile robots, Robotics and Autonomous Systems, Volume 88, February 2017, Pages 116-126, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.11.016.

We present a study of spatio-temporal environment representations and exploration strategies for long-term deployment of mobile robots in real-world, dynamic environments. We propose a new concept for life-long mobile robot spatio-temporal exploration that aims at building, updating and maintaining the environment model during the long-term deployment. The addition of the temporal dimension to the explored space makes the exploration task a never-ending data-gathering process, which we address by application of information-theoretic exploration techniques to world representations that model the uncertainty of environment states as probabilistic functions of time. We evaluate the performance of different exploration strategies and temporal models on real-world data gathered over the course of several months. The combination of dynamic environment representations with information-gain exploration principles allows to create and maintain up-to-date models of continuously changing environments, enabling efficient and self-improving long-term operation of mobile robots.

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Mapping (and navitating in) outdoor unstructured environments with low-cost and few sensors, using relations between landmarks instead of absolute or metrical positions

June 19, 2017 10:21 ,

Mark McClelland, Mark Campbell, Tara Estlin, Qualitative relational mapping and navigation for planetary rovers, Robotics and Autonomous Systems, Volume 83, 2016, Pages 73-86, ISSN 0921-8890, DOI: j.robot.2016.05.017.

This paper presents a novel method for qualitative mapping of large scale spaces which decouples the mapping problem from that of position estimation. The proposed framework makes use of a graphical representation of the world in order to build a map consisting of qualitative constraints on the geometric relationships between landmark triplets. This process allows a mobile robot to extract information about landmark positions using a set of minimal sensors in the absence of GPS. A novel measurement method based on camera imagery is presented which extends previous work from the field of Qualitative Spatial Reasoning. A Branch-and-Bound approach is taken to solve a set of non-convex feasibility problems required for generating off-line operator lookup tables and on-line measurements, which are fused into the map using an iterative graph update. A navigation approach for travel between distant landmarks is developed, using estimates of the Relative Neighborhood Graph extracted from the qualitative map in order to generate a sequence of landmark objectives based on proximity. Average and asymptotic performance of the mapping algorithm is evaluated using Monte Carlo tests on randomly generated maps, and a data-driven simulation is presented for a robot traversing the Jet Propulsion Laboratory Mars Yard while building a relational map. These results demonstrate that the system can be effectively used to build a map sufficiently complete and accurate for long-distance navigation as well as other applications.

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Improvements on the ICP algorithm to point cloud registration from a low precision RGB-D sensor

March 24, 2017 13:04 ,

Rogério Yugo Takimoto, Marcos de Sales Guerra Tsuzuki, Renato Vogelaar, Thiago de Castro Martins, André Kubagawa Sato, Yuma Iwao, Toshiyuki Gotoh, Seiichiro Kagei, 3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor, Mechatronics, Volume 35, May 2016, Pages 11-22, ISSN 0957-4158, DOI:j.mechatronics.2015.10.014.

A 3D reconstruction method using feature points is presented and the parameters used to improve the reconstruction are discussed. The precision of the 3D reconstruction is improved by combining point clouds obtained from different viewpoints using structured light. A well-known algorithm for point cloud registration is the ICP (Iterative Closest Point) that determines the rotation and translation that, when applied to one of the point clouds, places both point clouds optimally. The ICP algorithm iteratively executes two main steps: point correspondence determination and registration algorithm. The point correspondence determination is a module that, if not properly executed, can make the ICP converge to a local minimum. To overcome this drawback, two techniques were used. A meaningful set of 3D points using a technique known as SIFT (Scale-invariant feature transform) was obtained and an ICP that uses statistics to generate a dynamic distance and color threshold to the distance allowed between closest points was implemented. The reconstruction precision improvement was implemented using meaningful point clouds and the ICP to increase the number of points in the 3D space. The surface reconstruction is performed using marching cubes and filters to remove the noise and to smooth the surface. The factors that influence the 3D reconstruction precision are here discussed and analyzed. A detailed discussion of the number of frames used by the ICP and the ICP parameters is presented.

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