Simultaneous Localization and Mapping (SLAM) problem, where an autonomous vehicle moving in an unknown environment attempts to sense and map its surroundings while recognizing its own location and trajectory within the map, has always been a notable and popular research topic in the field of computer vision, robotics and artificial intelligence. Among the various types of solutions relying on different sensor modalities such as the global positioning system (GPS), radio signals, lidar, etc., vision-based solutions are of major interest nowadays because most cameras are low-cost and rich information gathering, especially for the stereo cameras. In this paper, different technologies of visual SLAM, where the main sensors are cameras, are surveyed with an emphasis on methodologies using stereo cameras. Some state-of-the-art open-source stereo visual SLAM frameworks are also discussed and compared. Finally, a general discussion of the challenges in terms of accuracy, processing time, cost, etc. is provided. The main purpose of this review is to provide a comprehensive overview of public available stereo visual SLAM frameworks and their corresponding pros and cons in different real-world scenarios.

Two-dimensional ultrasound (US) is widely used in minimally invasive cardiac procedures due to its convenience of use and noninvasive nature. However, the low quality of US images often limits their utility as a means for guiding procedures, since it is often difficult to relate the images to their anatomical context. To improve the interpretability of the US images while maintaining US as a flexible anatomical and functional real-time imaging modality, we describe a multimodality image navigation system that integrates 2D US images with their 3D context by registering them to high quality preoperative models based on magnetic resonance imaging (MRI) or computed tomography (CT) images. The mapping from such a model to the patient is completed using spatial and temporal registrations. Spatial registration is performed by a two-step rapid registration method that first approximately aligns the two images as a starting point to an automatic registration procedure. Temporal alignment is performed with the aid of electrocardiograph (ECG) signals and a latency compensation method. Registration accuracy is measured by calculating the TRE. Results show that the error between the US and preoperative images of a beating heart phantom is 1.7 +/-0.4 mm, with a similar performance being observed in in vivo animal experiments.

Dynamic cardiac magnetic resonance imaging (MR) and computed tomography (CT) provide cardiologists and cardiac surgeons with high-quality 4-D images for diagnosis and therapy, yet the effective use of these high-quality anatomical models remains a challenge. Ultrasound (US) is a flexible imaging tool, but the US images produced are often difficult to interpret unless they are placed within their proper 3-D anatomical context. The ability to correlate real-time 3-D US volumes (RT3D US) with dynamic MR/CT images would offer a significant contribution to improve the quality of cardiac procedures. In this paper, we present a rapid two-step method for registering RT3D US to high-quality dynamic 3-D MR/CT images of the beating heart. This technique overcomes some major limitations of image registration (such as the correct registration result not necessarily occurring at the maximum of the mutual information (MI) metric) using the MI metric. We demonstrate the effectiveness of our method in a dynamic heart phantom (DHP) study and a human subject study. The achieved mean target registration error of CT+US images in the phantom study is 2.59 mm. Validation using human MR/US volumes shows a target registration error of 1.76 mm. We anticipate that this technique will substantially improve the quality of cardiac diagnosis and therapies.

Two-dimensional or 3-D visual guidance is often used for minimally invasive cardiac surgery and diagnosis. This visual guidance suffers from several drawbacks such as limited field of view, loss of signal from time to time, and in some cases, difficulty of interpretation. These limitations become more evident in beating-heart procedures when the surgeon has to perform a surgical procedure in the presence of heart motion. In this paper, we propose dynamic 3-D virtual fixtures (DVFs) to augment the visual guidance system with haptic feedback, to provide the surgeon with more helpful guidance by constraining the surgeon's hand motions thereby protecting sensitive structures. DVFs can be generated from preoperative dynamic magnetic resonance (MR) or computed tomograph (CT) images and then mapped to the patient during surgery. We have validated the feasibility of the proposed method on several simulated surgical tasks using a volunteer's cardiac image dataset. Validation results show that the integration of visual and haptic guidance can permit a user to perform surgical tasks more easily and with reduced error rate. We believe this is the first work presented in the field of virtual fixtures that explicitly considers heart motion.

The lack of a potential field model capable of providing accurate representations of objects of arbitrary shapes is considered one major limitation in applying the artificial potential field method in many practical applications. In this correspondence, we propose a potential function based on generalized sigmoid functions. The generalized sigmoid model can be constructed from combinations of implicit primitives or from sampled surface data. The constructed potential field model can achieve an accurate analytic description of objects in two or three dimensions and requires very modest computation at run time. In this correspondence, applications of the generalized sigmoid model in path-planning tasks for mobile robots and in haptic feedback tasks are presented. The validation results in this correspondence show that the model can effectively allow the user or mobile robot to avoid penetrations of obstacles while successfully accomplishing the task.

This paper investigates the inherent oscillation problem of potential field methods (PFMs) in the presence of obstacles and in narrow passages. These problems can cause slow progress and system instability in implementation. To overcome these two problems, in this paper, we propose a modification of Newton's method. The use of the modified Newton's method, which applies anywhere C/sub 2/ continuous navigation functions are defined, greatly improves system performance when compared to the standard gradient descent approach. To the best of our knowledge, ours is the first systematic approach to the oscillation problems in PFMs. We have validated this technique by comparing its performance with the gradient descent method in obstacle-avoidance tasks with different potential models and parameter changes.

In this paper, we propose a solution for robot motion planning in a complex environment with sparsely placed obstacles of arbitrary shape. We propose a generalized sigmoid function method to model the obstacles of arbitrary shape. The proposed model can achieve an accurate analytic description of the obstacles in two or three dimensions and is very easy to calculate and visualize. To demonstrate our generalized sigmoid method, we consider a robot team goal-finding task in this paper. Simulation results show that a mobile robot team can reach, the goal through the complex environment with obstacles of arbitrary shape without collision with any obstacles and other team members.

Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. We present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. Validation using industry project data shows that the framework produces good results when used to predict software cost.

We consider potential field-based cooperative motion planning for a distributed team of semi-autonomous robots. We present a changing navigation function to allow the robots to incorporate new sensor data into their maps of the environment. We choose a Gaussian function to model attractors and a higher-order Gaussian-like function to model obstacles in order to avoid undesired local minima. Using arguments from hybrid systems theory, we show that this changing navigation function can be viewed as a mode-specific team Lyapunov function that stabilizes the system at all times. We. have verified our approach in simulations of a robot team mapping and foraging in an initially unknown environment. The team is able to map the environment, noting the location of all obstacles and attractive objects, then retrieve the attractors and return them to a goal position. Potential field navigation succeeds in this task while avoiding collisions between robots and obstacles as well as collisions among team members.