Category Archives: Robotics

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.

Posted in: Mobile robot mapping , Tagged: , ,

Dealing with multiple hypothesis in Graph-SLAM through multigraphs (as in multi-hierarchical graphs)

April 20, 2016 17:05 ,

Max Pfingsthorn and Andreas Birk, Generalized graph SLAM: Solving local and global ambiguities through multimodal and hyperedge constraints, The International Journal of Robotics Research May 2016 35: 601-630, DOI: 10.1177/0278364915585395.

Research in Graph-based Simultaneous Localization and Mapping has experienced a recent trend towards robust methods. These methods take the combinatorial aspect of data association into account by allowing decisions of the graph topology to be made during optimization. The Generalized Graph Simultaneous Localization and Mapping framework presented in this work can represent ambiguous data on both local and global scales, i.e. it can handle multiple mutually exclusive choices in registration results and potentially erroneous loop closures. This is achieved by augmenting previous work on multimodal distributions with an extended graph structure using hyperedges to encode ambiguous loop closures. The novel representation combines both hyperedges and multimodal Mixture of Gaussian constraints to represent all sources of ambiguity in Simultaneous Localization and Mapping. Furthermore, a discrete optimization stage is introduced between the Simultaneous Localization and Mapping frontend and backend to handle these ambiguities in a unified way utilizing the novel representation of Generalized Graph Simultaneous Localization and Mapping, providing a general approach to handle all forms of outliers. The novel Generalized Prefilter method optimizes among all local and global choices and generates a traditional unimodal unambiguous pose graph for subsequent continuous optimization in the backend. Systematic experiments on synthetic datasets show that the novel representation of the Generalized Graph Simultaneous Localization and Mapping framework with the Generalized Prefilter method, is significantly more robust and faster than other robust state-of-the-art methods. In addition, two experiments with real data are presented to corroborate the results observed with synthetic data. Different general strategies to construct problems from real data, utilizing the full representational power of the Generalized Graph Simultaneous Localization and Mapping framework are also illustrated in these experiments.

Posted in: Mobile robot SLAM , Tagged: ,

Interesting survey of relevant long-term applications of service robots in real environments

April 20, 2016 16:52 ,

Roberto Pinillos, Samuel Marcos, Raul Feliz, Eduardo Zalama, Jaime Gómez-García-Bermejo, Long-term assessment of a service robot in a hotel environment, Robotics and Autonomous Systems, Volume 79, May 2016, Pages 40-57, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.01.014.

The long term evaluation of the Sacarino robot is presented in this paper. The study is aimed to improve the robot‘s capabilities as a bellboy in a hotel; walking alongside the guests, providing information about the city and the hotel and providing hotel-related services. The paper establishes a three-stage assessment methodology based on the continuous measurement of a set of metrics regarding navigation and interaction with guests. Sacarino has been automatically collecting information in a real hotel environment for long periods of time. The acquired information has been analyzed and used to improve the robot’s operation in the hotel through successive refinements. Some interesting considerations and useful hints for the researchers of service robots have been extracted from the analysis of the results.

Posted in: Robotics , Tagged: ,

Integrating humans and robots in the factories

April 20, 2016 16:30 ,

Andrea Cherubini, Robin Passama, André Crosnier, Antoine Lasnier, Philippe Fraisse, Collaborative manufacturing with physical human–robot interaction, Robotics and Computer-Integrated Manufacturing, Volume 40, August 2016, Pages 1-13, ISSN 0736-5845, DOI: 10.1016/j.rcim.2015.12.007.

Although the concept of industrial cobots dates back to 1999, most present day hybrid human–machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human–robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

Posted in: Industrial robots , Tagged:

Very interesting survey on visual place recognition, including historical background, physio-psychological bases and a definition of “place” in robotics

April 20, 2016 15:45 ,

S. Lowry et al., Visual Place Recognition: A Survey, in IEEE Transactions on Robotics, vol. 32, no. 1, pp. 1-19, Feb. 2016. DOI: 10.1109/TRO.2015.2496823.

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

Posted in: Mobile robot SLAM , Tagged: ,

Incorporating spatial info into the symbolic (bag-of-words) info used for loop closure detection

April 20, 2016 15:34 ,

Nishant Kejriwal, Swagat Kumar, Tomohiro Shibata, High performance loop closure detection using bag of word pairs, Robotics and Autonomous Systems, Volume 77, March 2016, Pages 55-65, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.12.003.

In this paper, we look into the problem of loop closure detection in topological mapping. The bag of words (BoW) is a popular approach which is fast and easy to implement, but suffers from perceptual aliasing, primarily due to vector quantization. We propose to overcome this limitation by incorporating the spatial co-occurrence information directly into the dictionary itself. This is done by creating an additional dictionary comprising of word pairs, which are formed by using a spatial neighborhood defined based on the scale size of each point feature. Since the word pairs are defined relative to the spatial location of each point feature, they exhibit a directional attribute which is a new finding made in this paper. The proposed approach, called bag of word pairs (BoWP), uses relative spatial co-occurrence of words to overcome the limitations of the conventional BoW methods. Unlike previous methods that use spatial arrangement only as a verification step, the proposed method incorporates spatial information directly into the detection level and thus, influences all stages of decision making. The proposed BoWP method is implemented in an on-line fashion by incorporating some of the popular concepts such as, K-D tree for storing and searching features, Bayesian probabilistic framework for making decisions on loop closures, incremental creation of dictionary and using RANSAC for confirming loop closure for the top candidate. Unlike previous methods, an incremental version of K-D tree implementation is used which prevents rebuilding of tree for every incoming image, thereby reducing the per image computation time considerably. Through experiments on standard datasets it is shown that the proposed methods provide better recall performance than most of the existing methods. This improvement is achieved without making use any geometric information obtained from range sensors or robot odometry. The computational requirements for the algorithm is comparable to that of BoW methods and is shown to be less than the latest state-of-the-art method in this category.

Posted in: Mobile robot SLAM , Tagged: ,

Implementation of spatial relations in graph-SLAM through quaternions instead of homogeneous matrices

April 20, 2016 15:31 ,

Jiantong Cheng, Jonghyuk Kim, Zhenyu Jiang, Wanfang Che, Dual quaternion-based graphical SLAM, Robotics and Autonomous Systems, Volume 77, March 2016, Pages 15-24, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.12.001.

This paper presents a new parameterization approach for the graph-based SLAM problem and reveals the differences of two popular over-parameterized ways in the optimization procedure. In the SALM problem, constraints or relative transformations between any two poses are generally separated into translations plus 3D rotations, which are then described in a homogeneous transformation matrix (HTM) to simplify computational operations. This however introduces added complexities in frequent conversions between the HTM and state variables, due to their different representations. This new approach, unit dual quaternion (UDQ), describes a spatial transformation as a screw with only 8 elements. We show that state variables can be directly represented by UDQs, and how their relative transformations can be written with the UDQ product, without the trivial computations of HTM. Then, we explore the performances of the unit quaternion and the axis–angle representations in the graph-based SLAM problem, which have been successfully applied to over parameterize perturbations under the assumption of small errors. Based on public synthetic and real-world datasets in 2D and 3D environments, experimental results show that the proposed approach reduces greatly the computational complexity while obtaining the same optimization accuracies as the HTM-based algorithm, and the axis–angle representation is superior to be the quaternion in the case of poor initial estimations.

Posted in: Mobile robot SLAM , Tagged: ,

Interesting approach to PF-based localization and active localization when the map contains semantic information

February 24, 2016 18:28 ,

Nikolay Atanasov, Menglong Zhu, Kostas Daniilidis, and George J. Pappas, Localization from semantic observations via the matrix permanent, The International Journal of Robotics Research January–March 2016 35: 73-99, first published on October 6, 2015, DOI: 10.1177/0278364915596589.

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization.

Posted in: Mobile Robot Localization, Semantic maps for robots , Tagged: ,

Using the Bingham distribution of probability, which is defined on a d-dimensional sphere to be antipodally symmetric, to address the problem of angle periodicity in [0,2pi] when estimating orientation in a recursive filter

February 4, 2016 16:44 ,

Gilitschenski, I.; Kurz, G.; Julier, S.J.; Hanebeck, U.D., Unscented Orientation Estimation Based on the Bingham Distribution, in Automatic Control, IEEE Transactions on , vol.61, no.1, pp.172-177, Jan. 2016, DOI: 10.1109/TAC.2015.2423831.

In this work, we develop a recursive filter to estimate orientation in 3D, represented by quaternions, using directional distributions. Many closed-form orientation estimation algorithms are based on traditional nonlinear filtering techniques, such as the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). These approaches assume the uncertainties in the system state and measurements to be Gaussian-distributed. However, Gaussians cannot account for the periodic nature of the manifold of orientations and thus small angular errors have to be assumed and ad hoc fixes must be used. In this work, we develop computationally efficient recursive estimators that use the Bingham distribution. This distribution is defined on the hypersphere and is inherently more suitable for periodic problems. As a result, these algorithms are able to consistently estimate orientation even in the presence of large angular errors. Furthermore, handling of nontrivial system functions is performed using an entirely deterministic method which avoids any random sampling. A scheme reminiscent of the UKF is proposed for the nonlinear manifold of orientations. It is the first deterministic sampling scheme that truly reflects the nonlinear manifold of orientations.

Posted in: Bayesian filtering, Mobile Robot Localization , Tagged: ,

Robotic probabilistic SLAM in continuous time

December 22, 2015 19:43 ,

Furgale1 P., Tong C.-H., Barfoot T.-D., Sibley G., Continuous-time batch trajectory estimation using temporal basis functions, The International Journal of Robotics Research December 2015 vol. 34 no. 14 1688-1710, DOI: 10.1177/0278364915585860.

Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable; however, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units, rolling-shutter cameras, or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full Maximum-a-Posteriori estimation problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian and show how the coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms through the application of different assumptions at any point. We use the simultaneous localization and mapping problem as our motivation throughout the paper, although the approach is not specific to this application. Results from two experiments are provided to validate the approach: (i) self-calibration involving a camera and a high-rate inertial measurement unit, and (ii) perspective localization with a rolling-shutter camera.

Posted in: Mobile robot SLAM , Tagged: