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: ,

Theoretical models for explaining the human (quick) decicion-making process

April 20, 2016 16:42 ,

Roger Ratcliff, Philip L. Smith, Scott D. Brown, Gail McKoon, Diffusion Decision Model: Current Issues and History, Trends in Cognitive Sciences, Volume 20, Issue 4, April 2016, Pages 260-281, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.01.007.

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology.

Posted in: Psycho-physiological bases of engineering , Tagged:

Cognitive Models as Bridge between Brain and Behavior

April 20, 2016 16:40 ,

Bradley C. Love, Cognitive Models as Bridge between Brain and Behavior, Trends in Cognitive Sciences, Volume 20, Issue 4, April 2016, Pages 247-248, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.02.006.

How can disparate neural and behavioral measures be integrated? Turner and colleagues propose joint modeling as a solution. Joint modeling mutually constrains the interpretation of brain and behavioral measures by exploiting their covariation structure. Simultaneous estimation allows for more accurate prediction than would be possible by considering these measures in isolation.

Posted in: Cognitive sciences , 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:

Incremental (hierarchical) search for the optimal policy on markov decision processes

April 20, 2016 16:26 ,

Vu Anh Huynh, Sertac Karaman, and Emilio Frazzoli, An incremental sampling-based algorithm for stochastic optimal control, The International Journal of Robotics Research April 2016 35: 305-333, DOI: 10.1177/0278364915616866.

In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Using the Markov chain approximation method and recent advances in sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process to incrementally compute control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise.

Posted in: Control Engineering , Tagged:

The diverse roles of the hippocampus

April 20, 2016 16:16 ,

Daniel Bendor, Hugo J. Spiers, Does the Hippocampus Map Out the Future?, Trends in Cognitive Sciences, Volume 20, Issue 3, March 2016, Pages 167-169, ISSN 1364-6613, DOI: 10.1016/j.tics.2016.01.003.

Decades of research have established two central roles of the hippocampus – memory consolidation and spatial navigation. Recently, a third function of the hippocampus has been proposed: simulating future events. However, claims that the neural patterns underlying simulation occur without prior experience have come under fire in light of newly published data.

Posted in: Psycho-physiological bases of engineering , 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: ,