Large Real-world Environment Research Articles

Three-Way Decisions on Streaming Computing Platforms Supporting Decision-Making in Complex Large Real-World Environments

In complex environments, decision-making processes are more and more dependent on gathering, processing and analysis of huge amounts of data, often produced with different velocities and different formats by distributed sensors (human or automatic). Such streams of data also suffer of imprecision and uncertainty. On the other hand, Three-way Decision is considered a suitable approach for data analysis based on the tri-partitioning of the universe of discourse, i.e., exploiting the notions of acceptance, rejection and non-commitment, as well as the human brain does to solve numerous problems. Suppose the application scenario foresees the processing of data streams. In that case, the analysis task could be accomplished by considering the stream computing paradigm which is one of the most important paradigms in Big Data. With such a paradigm data arrives, is processed and departs in real-time without needing to be temporarily serialized into a storage system. This work analyzes the implementation of the Three-Way Decision approach, based on Rough Set Theory, on a real-time data processing platform supporting streaming computing, i.e., Apache Spark.

Bio-inspired homogeneous multi-scale place recognition

Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.

SceneGrok

With modern computer graphics, we can generate enormous amounts of 3D scene data. It is now possible to capture high-quality 3D representations of large real-world environments. Large shape and scene databases, such as the Trimble 3D Warehouse, are publicly accessible and constantly growing. Unfortunately, while a great amount of 3D content exists, most of it is detached from the semantics and functionality of the objects it represents. In this paper, we present a method to establish a correlation between the geometry and the functionality of 3D environments. Using RGB-D sensors, we capture dense 3D reconstructions of real-world scenes, and observe and track people as they interact with the environment. With these observations, we train a classifier which can transfer interaction knowledge to unobserved 3D scenes. We predict a likelihood of a given action taking place over all locations in a 3D environment and refer to this representation as an action map over the scene. We demonstrate prediction of action maps in both 3D scans and virtual scenes. We evaluate our predictions against ground truth annotations by people, and present an approach for characterizing 3D scenes by functional similarity using action maps.

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

The WALKABOUT: Using virtual environments to assess large-scale spatial abilities

Computer-simulated virtual environments (VEs) offer promise for assessing people's spatial abilities in large real-world environments. This paper introduces the WALKABOUT, a VE-based test that simulates navigation through large spaces. Participants' ability to form an accurate mental representation of a familiar large-scale environment correlated nearly as highly with their performance on the WALKABOUT as it did with their self-reported sense of direction. Performance on two subscales of the test further indicated that the ability to account for changing egocentric relationships as a result of self-movement and the ability to recognize a novel perspective on an environment are both significantly related to spatial ability at the environmental scale.

Individual differences in spatial learning from computer-simulated environments.

A multivariate study examined relationships between the following factors: paper-and-pencil assessments of verbal and spatial ability, ability to form an accurate spatial representation of a large real-world environment, gender, computer attitudes and experience, proficiency with the navigational interface of a virtual environment (VE), and the ability to acquire and transfer spatial knowledge from a VE. Psychometrically assessed spatial ability and proficiency with the navigational interface were found to make substantial contributions to individual differences in the ability to acquire spatial information from a VE. Gender influenced many VE-related tasks, primarily through its relationship with interface proficiency and spatial ability. Measures of spatial knowledge of a VE maze were highly predictive of subsequent performance in a similar real-world maze, suggesting that VEs can be useful for training people about real-world spaces.

Individual differences in spatial learning from computer-simulated environments.

A multivariate study examined relationships between the following factors: paper-and-pencil assessments of verbal and spatial ability, ability to form an accurate spatial representation of a large real-world environment, gender, computer attitudes and experience, proficiency with the navigational interface of a virtual environment (VE), and the ability to acquire and transfer spatial knowledge from a VE. Psychometrically assessed spatial ability and proficiency with the navigational interface were found to make substantial contributions to individual differences in the ability to acquire spatial information from a VE. Gender influenced many VE-related tasks, primarily through its relationship with interface proficiency and spatial ability. Measures of spatial knowledge of a VE maze were highly predictive of subsequent performance in a similar real-world maze, suggesting that VEs can be useful for training people about real-world spaces.