We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.

Simultaneous localization and mapping (SLAM) is a well-studied problem in mobile robotics. However, the majority of the proposed techniques for SLAM rely on the use of accurate and dense measurements provided by laser rangefinders to correctly localize the robot and produce accurate and detailed maps of complex environments. Little work has been done on the use of low-cost but noisy and sparse sonar sensors for SLAM in large indoor environments involving large loops. In this paper, we present our approach to SLAM with sonar sensors by applying particle filtering and a line-segment-based map representation with an orthogonality assumption to map indoor environments much larger and more challenging than those previously considered with sonar sensors. Results from robotic experiments demonstrate that it is possible to produce good maps of large indoor environments with large loops despite the inherent limitations of sonar sensors.

Motion and sensor models are crucial components in current algorithms for mobile robot localization and mapping. These models are typically provided and hand-tuned by a human operator and are often derived from intensive and careful calibration experiments and the operator's knowledge and experience with the robot and its operating environment. In this paper, we demonstrate how the parameters of both the motion and sensor models can be automatically estimated during normal robot operations via machine learning methods thereby eliminating the necessity of manually tuning these models through a laborious calibration process. Results from real-world robotic experiments are presented that show the effectiveness of the estimation approach.

The WEBSOM methodology, proven effective for building very large text archives, includes a method that extracts labels for each document cluster assigned to nodes in the map. However, the WEBSOM method needs to retrieve all the words of all the documents associated to each node. Since maps may have more than 100,000 nodes and since the archive may contain up to seven million documents, the WEBSOM methodology needs a faster alternative method for keyword selection. Presented here is such an alternative method that is able to quickly deduce meaningful labels per node in the map. It does this just by analyzing the relative weight distribution of the SOM weight vectors and by taking advantage of some characteristics of the random projection method used in dimensionality reduction. The effectiveness of this technique is demonstrated on news document collections.

The Hopfield and bi-directional associative memory (BAM) models are well developed and carefully studied models for associative memory that are patterned after the memory structure of the animal brain. Their basic limitation is that they can only perform associations between at most two sets of patterns. Several different models for generalized associative memory are proposed. These models are all extensions of the Hopfield and BAM models that can perform multiple associations. Extensive software simulations are conducted to evaluate the different models, using memory capacity as the basis for comparing their performance. The use of the Widrow-Hoff gradient descent error correction algorithm is introduced that can improve the memory capacities of the various models. A potential application of these models as data fusion systems is explored.

Self-Organizing Maps, being used mainly with data that are not pre-labeled, need automatic procedures for extracting keywords as labels for each of the map units. The WEBSOM methodology for building very large text archives has a very slow method for extracting such unit labels. It computes the relative frequencies of all the words of all the documents associated to each unit and then compares these to the relative frequencies of all the words of all the other units of the map. Since maps may have more than 100,000 units and the archive may contain up to 7 million documents, the existing WEBSOM method is not practical. This paper describes how the meaningful labels per map unit can be deduced by analyzing the relative weight distribution of the SOM weight vectors and by taking advantage of some characteristics of the random projection method used in dimensionality reduction. The effectiveness of this technique is demonstrated on archives of the well studied Reuters and CNN collections. Comparisons with the WEBSOM method are provided.

Not only have self-organizing maps (SOMs), such as the WEBSOM, been shown to scale up to very large datasets, these maps also allow for a novel mode of navigating through a large collection of text documents. The entire text collection is presented to a user as a regular map, where each point in the map is associated to a group of documents that are likely to be composed of similar terms and phrases. In addition, the closer two points are in the map, the more similar are their respective associated documents. Thus, once an interesting document is found in the map, the user just has to click around the vicinity of that document to retrieve other similar documents. A major drawback of SOMs, however, is the long training time required, especially for document collections where both the volume and the dimensionality are huge. We demonstrate how the size of the initial text collection is progressively and drastically reduced from the raw document collection to the final SOM-based text archive. We demonstrate this using a widely studied Reuters collection.

The WEBSOM methodology for building very large text archives has a very slow method for extracting meaningful unit labels. This is because the method computes for the relative frequencies of all the words of all the documents associated to each unit and then compares these to the relative frequencies of all the words of all the other units of the map. Since maps may have more than 100,000 units and the archive may contain up to 7 million documents, the existing WEBSOM method is not practical. A fast alternative method is based on the distribution of weights in the weight vectors of the trained map, plus a simple manipulation of the random projection matrix used for input data compression. Comparisons made using a WEBSOM archive of the Reuters text collection reveal that a high percentage of keywords extracted using this method match the keywords extracted for the same map units using the original WEBSOM method.