The RatSLAM project : robot spatial navigation

Rats are superior to the most advanced robots when it comes to creating and exploiting spatial representations. A wild rat can have a foraging range of hundreds of meters, possibly kilometers, and yet the rodent can unerringly return to its home after each foraging mission, and return to profitable foraging locations at a later date (Davis, et al., 1948). The rat runs through undergrowth and pipes with few distal landmarks, along paths where the visual, textural, and olfactory appearance constantly change (Hardy and Taylor, 1980; Recht, 1988). Despite these challenges the rat builds, maintains, and exploits internal representations of large areas of the real world throughout its two to three year lifetime. While algorithms exist that allow robots to build maps, the questions of how to maintain those maps and how to handle change in appearance over time remain open. The robotic approach to map building has been dominated by algorithms that optimise the geometry of the map based on measurements of distances to features. In a robotic approach, measurements of distance to features are taken with range-measuring devices such as laser range finders or ultrasound sensors, and in some cases estimates of depth from visual information. The features are incorporated into the map based on previous readings of other features in view and estimates of self-motion. The algorithms explicitly model the uncertainty in measurements of range and the measurement of self-motion, and use probability theory to find optimal solutions for the geometric configuration of the map features (Dissanayake, et al., 2001; Thrun and Leonard, 2008). Some of the results from the application of these algorithms have been impressive, ranging from three-dimensional maps of large urban strucutures (Thrun and Montemerlo, 2006) to natural environments (Montemerlo, et al., 2003).

Loop closure detection on a suburban road network using a continuous appearance-based trajectory

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives occur, demonstrating its potential use for robust loop closure detection in large environments.

Improving recall in appearance-based visual SLAM using visual expectation

In this paper, we present a new algorithm for boosting visual template recall performance through a process of visual expectation. Visual expectation dynamically modifies the recognition thresholds of learnt visual templates based on recently matched templates, improving the recall of sequences of familiar places while keeping precision high, without any feedback from a mapping backend. We demonstrate the performance benefits of visual expectation using two 17 kilometer datasets gathered in an outdoor environment at two times separated by three weeks. The visual expectation algorithm provides up to a 100% improvement in recall. We also combine the visual expectation algorithm with the RatSLAM SLAM system and show how the algorithm enables successful mapping

Graph rigidity for near-coplanar structure from motion

Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.

Face recognition across pose on video using eigen light-fields

We propose an approach to employ eigen light-fields for face recognition across pose on video. Faces of a subject are collected from video frames and combined based on the pose to obtain a set of probe light-fields. These probe data are then projected to the principal subspace of the eigen light-fields within which the classification takes place. We modify the original light-field projection and found that it is more robust in the proposed system. Evaluation on VidTIMIT dataset has demonstrated that the eigen light-fields method is able to take advantage of multiple observations contained in the video.

Learning informative features for indoor traversability

This paper presents a method for automatic terrain classification, using a cheap monocular camera in conjunction with a robot’s stall sensor. A first step is to have the robot generate a training set of labelled images. Several techniques are then evaluated for preprocessing the images, reducing their dimensionality, and building a classifier. Finally, the classifier is implemented and used online by an indoor robot. Results are presented, demonstrating an increased level of autonomy.

Gaussian process models for sensor-centric robot localisation

This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

Using incomplete online metric maps for topological exploration with the Gap Navigation Tree

This paper presents a general, global approach to the problem of robot exploration, utilizing a topological data structure to guide an underlying Simultaneous Localization and Mapping (SLAM) process. A Gap Navigation Tree (GNT) is used to motivate global target selection and occluded regions of the environment (called “gaps”) are tracked probabilistically. The process of map construction and the motion of the vehicle alters both the shape and location of these regions. The use of online mapping is shown to reduce the difficulties in implementing the GNT.

Overview of INEX 2008 Link the Wiki Track

The Link the Wiki track at INEX 2008 offered two tasks, file-to-file link discovery and anchor-to-BEP link discovery. In the former 6600 topics were used and in the latter 50 were used. Manual assessment of the anchor-to-BEP runs was performed using a tool developed for the purpose. Runs were evaluated using standard precision & recall measures such as MAP and precision / recall graphs. 10 groups participated and the approaches they took are discussed. Final evaluation results for all runs are presented.

Planning most-likely paths from overhead imagery

This paper is about planning paths from overhead imagery, the novelty of which is taking explicit account of uncertainty in terrain classification and spatial variation in terrain cost. The image is first classified using a multi-class Gaussian Process Classifier which provides probabilities of class membership at each location in the image. The probability of class membership at a particular grid location is then combined with a terrain cost evaluated at that location using a spatial Gaussian process. The resulting cost function is, in turn, passed to a planner. This allows both the uncertainty in terrain classification and spatial variations in terrain costs to be incorporated into the planned path. Because the cost of traversing a grid cell is now a probability density rather than a single scalar value, we can produce not only the most-likely shortest path between points on the map, but also sample from the cost map to produce a distribution of paths between the points. Results are shown in the form of planned paths over aerial maps, these paths are shown to vary in response to local variations in terrain cost.

Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

Conformance checking of service behavior

A service-oriented system is composed of independent software units, namely services, that interact with one another exclusively through message exchanges. The proper functioning of such system depends on whether or not each individual service behaves as the other services expect it to behave. Since services may be developed and operated independently, it is unrealistic to assume that this is always the case. This article addresses the problem of checking and quantifying how much the actual behavior of a service, as recorded in message logs, conforms to the expected behavior as specified in a process model.We consider the case where the expected behavior is defined using the BPEL industry standard (Business Process Execution Language for Web Services). BPEL process definitions are translated into Petri nets and Petri net-based conformance checking techniques are applied to derive two complementary indicators of conformance: fitness and appropriateness. The approach has been implemented in a toolset for business process analysis and mining, namely ProM, and has been tested in an environment comprising multiple Oracle BPEL servers.

Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

Force controlled hexapod walking

In this paper we describe the dynamic simulation of an 18 degrees of freedom hexapod robot with the objective of developing control algorithms for smooth, efficient and robust walking in irregular terrain. This is to be achieved by using force sensors in addition to the conventional joint angle sensors as proprioceptors. The reaction forces on the feet of the robot provide the necessary information on the robots interaction with the terrain. As a first step we validate the simulator by implementing movement control by joint torques using PID controllers. As an unexpected by-product we find that it is simple to achieve robust walking behaviour on even terrain for a hexapod with the help of PID controllers and by specifying a trajectory of only a few joint configurations.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.