A Rao-Blackwellized particle filter approach is an effective means to estimate the full SLAM posterior. The approach provides for the use of raw sensor measurements directly in SLAM, thus obviating the need to extract landmarks using complex feature extraction methods and data association. In this paper a solution framework based on Rao-Blackwellized particle filters (RB) and genetic algorithms (GA) is proposed for recovering the full SLAM posterior using raw exteroceptive sensor measurements, i.e. without landmarks. The resultant Rao-Blackwellized genetic algorithmic filter (RBGAF) permits the uses of any arbitrary measurement model unlike FastSLAM with scan matching. Since the proposed method represents the environmental map state for each robot trajectory using a population of chromosomes as opposed to grids, RBGAF is much more memory efficient than DP-SLAM. Memory efficiency is further enhanced through the exploitation of dynamic data structures for representing the maps and the robot trajectories. This makes the proposed RBGAF very suitable for large scale SLAM in 3D environments. Further, the proposed method's provision for adaptation of chromosome lifetime/group sizes and its ability to incorporate alternative map representations makes it adaptable to varied environments and different sensors. Simulation and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

Manipulation tasks performed under microscopes are fatigue inducing on account of the difficulty in visualisation of the scene and hand-eye coordination. This work concentrates on providing assistance to the operator in the form of a visual-haptic interactive system which allows intuitive command of the manipulator motion axes. Specific examples of visualisation tools are presented which compensate for problems such as: spatial d is orientation, loss of depth perception, and occluded data.

Simultaneous localization and mapping (SLAM) for mobile robots has attracted research interests in the past two decades. Recent years, Rao-Blackwellized particle filter (RBPF) approach proved to be an effective means to estimate the full SLAM posterior. However, most of the SLAM practices are implemented in static environment. To navigate in dynamic environment, model based approaches have been implemented. The approaches require modeling, classification, and data association. Modeling and classification often require prior knowledge of the environment and are less robust in highly complex environment. To obviate modeling, classification and data association in dynamic environment, a solution framework based on Rao-Blackwellised genetic algorithmic filter (RBGAF) is extended for recovering the full SLAM posterior, using raw sensor measurements. The resultant extended RBGAF-SLAM recovers the negative sensor information during SLAM so that the moving objects can be efficiently identified in the particle maps. This approach permits the implementation of SLAM without prior knowledge of the environment and the static environment assumption. Simulations and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

This paper presents a genetic algorithmic filter (GAF) approach to mobile robot simultaneous localization and mapping (SLAM). A Genetic algorithmic approach is used to solve the SLAM problem by concurrently optimizing appropriate cost functions defined over two sets of chromosome populations representing the robot pose and the environmental map. As such the methodology has the potential to produce globally consistent solutions to this highly non-linear, non-Gaussian SLAM problem. Similar to Monte-Carlo probabilistic localization, particles or chromosomes are used to represent the belief state of the robot and the environmental map. However, unlike in the case of a particle filter approach such as FastSLAM, the genetic algorithmic approach presented in the paper does not rely on environmental feature extraction and data association. Further, it is shown how the problems of sample impoverishment associated with re-sampling which is common in a particle filter approach can be systematically and effectively overcome through the application of a parallel variant of the genetic algorithm with associated operators. Simulation and experimental results are presented to demonstrate definite performance gains achievable through the use of GAF-SLAM and its potential to yield globally consistent SLAM results

This paper presents details of techniques for projecting image data from microscope views to perspective views or model spaces. These software tools are used to assist human operators with visualisation and navigation in semi-autonomous tasks performed at the micro-scale. The problems of applying computer vision techniques at this scale are highlighted. The solution ensures minimal reliance on prior calibration, and overcomes the problems of finding correspondences over scale differences by accumulating data in image mosaics.

A technique suitable for tracking rigid motion in microscope image sequences is presented. Microscope images have particular problems: notably sparsity of features and ambiguity of motion. The method searches for point correspondences but concentrates on random transitory features rather than regular geometry. The paper highlights the problems that must be faced and details the work that has been implemented to combine robustness with efficiency. The output of the search algorithm are sets of correspondences and the inter-image transformations.

The problems of operator interaction in micro-assembly tasks are addressed. The solution is a visualisation and tracking system based on techniques from computer vision and robotics. The man-machine-interface leaves the operator free to concentrate on the task whilst the machine handles the problems of precision and scale. The benefits of this approach are increased productivity and precision. An example of an implementation is presented that concentrates on tracking and navigation from a perspective view. This illustrates principles of robustness in feature tracking, parameter estimation and model reconstruction. Estimation is then followed by bundle adjustment , which enforces a parameterisation consistent with a motion model. The mathematical framework reduces dependency on explicit calibration of system parameters, using the constraints between motion kinematics and multiple view geometry.