An efficient and accurate method for the relaxation of multiview registration error

This paper presents a new method for the relaxation of multiview registration error. The multiview registration problem is represented using a graph. Each node and each edge in the graph represents a 3-D data set and a pairwise registration, respectively. Assuming that all the pairwise registration processes have converged to fine results, this paper shows that the multiview registration problem can be converted into a quadratic programming problem of Lie algebra parameters. The constraints are obtained from every cycle of the graph to eliminate the accumulation errors of global registration. A linear solution is proposed to distribute the accumulation error to proper positions in the graph, as specified by the quadratic model. Since the proposed method does not involve the original 3-D data, it has low time and space complexity. Additionally, the proposed method can be embedded into a trust-region algorithm and, thus, can correctly handle the nonlinear effects of large accumulation errors, while preserving the global convergence property to the first-order critical point. Experimental results confirm both the efficiency and the accuracy of the proposed method.

Mobile robot localization using the Hausdorff distance

This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.

Trajectory Optimization using Reinforcement Learning for Map Exploration

Automatically building maps from sensor data is a necessary and fundamental skill for mobile robots; as a result, considerable research attention has focused on the technical challenges inherent in the mapping problem. While statistical inference techniques have led to computationally efficient mapping algorithms, the next major challenge in robotic mapping is to automate the data collection process. In this paper, we address the problem of how a robot should plan to explore an unknown environment and collect data in order to maximize the accuracy of the resulting map. We formulate exploration as a constrained optimization problem and use reinforcement learning to find trajectories that lead to accurate maps. We demonstrate this process in simulation and show that the learned policy not only results in improved map building, but that the learned policy also transfers successfully to a real robot exploring on MIT campus.

A Novel Measure of Uncertainty for Mobile Robot SLAM with Rao—Blackwellized Particle Filters

Rao—Blackwellized particle filters (RBPFs) are an implementation of sequential Bayesian filtering that has been successfully applied to mobile robot simultaneous localization and mapping (SLAM) and exploration. Measuring the uncertainty of the distribution estimated by a RBPF is required for tasks such as information gain-guided exploration or detecting loop closures in nested loop environments. In this paper we propose a new measure that takes the uncertainty in both the robot path and the map into account. Our approach relies on the entropy of the expected map (EM) of the RBPF, a new variable built by integrating the map hypotheses from all of the particles. Unlike previous works that use the joint entropy of the RBPF for active exploration, our proposal is better suited to detect opportunities to close loops, a key aspect to reduce the robot path uncertainty and consequently to improve the quality of the maps being built. We provide a theoretical discussion and experimental results with real data that support our claims.

Combinatorial and probabilistic fusion of noisy correlation measurements for untracked freehand 3-D ultrasound

In freehand 3-D ultrasound (US), the relative positions of US images are usually measured using a position tracking device despite its cumbersome nature. The probe trajectory can instead be estimated from image data, using registration techniques to recover in-plane motion and speckle decorrelation to recover out-of-plane transformations. The relationship between speckle decorrelation and elevational separation is typically represented by a single curve, estimated from calibration data. Distances read off such a curve are corrupted by bias and uncertainty, and only provide an absolute estimate of elevational displacement. This paper presents a probabilistic model of the relationship between correlation measurements and elevational separation. This representation captures the skewed distribution of distance estimates based on high correlations and the uncertainties attached to each measurement. Multiple redundant correlation measurements can then be integrated within a maximum likelihood estimation framework. This paper also introduces a new method based on the traveling salesman problem for resolving sign ambiguities in data sets resulting from nonmonotonic probe motion and frame intersections. Experiments with real and synthetic US data show that by combining these new methods, out-of-plane US probe motion is recovered with improved accuracy over baseline methods using a deterministic model and fewer measurements.

Real Time Mapping and Dynamic Navigation for Mobile Robots

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.

Probabilistic speckle decorrelation for 3D ultrasound

Recent developments in freehand 3D ultrasound (US) have shown how image registration and speckle decorrelation methods can be used for 3D reconstruction instead of relying on a tracking device. Estimating elevational separation between untracked US images using speckle decorrelation is error prone due to the uncertainty that plagues the correlation measurements. In this paper, using maximum entropy estimation methods, the uncertainty is directly modeled from the calibration data normally used to estimate an average decorrelation curve. Multiple correlation measurements can then be fused within a maximum likelihood estimation framework in order to reduce the drift in elevational pose estimation over large image sequences. The approach is shown to be effective through empirical results on simulated and phantom US data.

Localisation and World Modelling: An Architectural Perspective

Autonomous robot world modelling is a “chicken-and-egg” problem: position estimation needs a model of the world, whereas world modelling needs the robot position. Most of the works dealing with this issue propose holistic solutions under an algorithmic perspective by neglecting software architecture issues. This results in huge and monolithic pieces of software where implementation details reify strategic decisions. An architectural approach founded on separation of concerns may help to break the loop. Localisation and modelling, acting on different time scales, are mostly independent of each other. Sometimes synchronisation is required. Whenever needed, an external strategy tunes the relative rates of the two activities. The paper introduces rationale, design, and implementation of such a system which relies on Real-Time Performers, a software architecture providing suitable architectural abstractions to observe and control the system's temporal behaviour.

Qualitative map learning based on covisibility of objects

Autonomous map construction is one of the most fundamental and significant issues in intelligent mobile robot research. While a variety of map construction methods have been proposed, most require some quantitative measurements of the environment and a mechanism of precise self-localization. This paper proposes a novel map construction method using only qualitative information about "how often two objects are observed simultaneously." This method is based on heuristics--"closely located objects are likely to be seen simultaneously more often than distant objects" and a well-known multivariate data analysis technique-multidimensional scaling. A significant feature of this method is that it requires neither quantitative sensor measurements nor information about the robot's own position. Simulation and experimental results demonstrated that this method is sufficiently practical for capturing a qualitative spatial relationship among identifiable landmark objects rapidly.

Multiview registration of 3D scenes by minimizing error between coordinate frames

This paper addresses the problem of large-scale multiview registration of range images captured from unknown viewing directions. To reduce the computational burden, we separate the local problem of pairwise registration on neighboring views from the global problem of distribution of accumulated errors. We define the global problem as an optimization over the graph of neighboring views, and we show how the graph can be decomposed into a set of cycles such that the optimal transformation parameters for each cycle can be solved in closed form. We then describe an iterative procedure that can be used to integrate the solutions for the set of cycles across the graph of views. This method for error distribution does not require point correspondences between views, and can be used to integrate any method of pairwise registration or robot odometry.