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Abstract-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.

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. In this paper, 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.

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 CMV collections. Comparisons with the WEBSOM method are provided.

Abstract-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.