Recent developments and trends in point set registration methods

As one of the most important issues in the field of mobile robotics, self-localization allows a mobile robot to identify and keep track of its own position and orientation as the robot moves through the environment. In this work, a hybrid localization approach based on the particle filter and particle swarm optimization algorithm is presented, focusing on the localization tasks when an a priori environment map is available. This results an accurate and robust particle filter based localization algorithm that is able to work in symmetrical environments. The performance of the proposed approach has been evaluated for indoor robot localization and compared with two benchmark algorithms. The experimental results show that the proposed method achieves robust and accurate positioning results in indoor environments, requiring fewer particles than the benchmark methods. This advance could be integrated in a wide range of mobile robot systems, helping to reduce the computational cost and improve the navigation efficiency.

An improved particle filter for mobile robot localization based on particle swarm optimization

We propose an effective human comfortable safety framework enabling a mobile service robot to navigate safely and socially in social environments. The proposed framework takes human states (position, orientation, motion and hand poses) and social interaction information relative to the robot into account to model extended personal space and social interaction space, respectively, the combination of which results in a dynamic social zone (DSZ). The DSZ-based human comfortable safety framework is able to estimate an approaching goal pose of the robot for a human or a group of humans, thus allowing the robot to not only avoid but also to approach a human or a group of humans in a socially acceptable manner. The DSZ is incorporated into the robots motion planning system comprising the D* planner technique and dynamic window approach algorithm to generate motion control commands for the mobile robot. We verify the effectiveness of the proposed method through simulation and experimental results under the newly proposed human comfortable safety indices.

Dynamic Social Zone based Mobile Robot Navigation for Human Comfortable Safety in Social Environments

Robotic grinding is a promising technique to generate the final shape of blades. It can relieve human from participating in dirty and noisy environments, improve product quality, and lower production costs. One important task in robotic grinding is 3-D shape matching. However, existing matching methods do not consider the requirements associated with different grinding allowances, which can potentially lead to an unstable grinding force. This paper proposes a novel shape matching method for robotic grinding. The goal is to define a new objective function considering different allowance weights for stable grinding, and address incorrect shape matching from the missing points or uneven density points. The main contribution of this paper is the application of variance minimization to construct an objective function, from which the required shape matching parameters are iteratively calculated. This method balances the contributions of all the measured points, weighs the allowances for the pressure and suction surfaces of a blade, and avoids incorrect matching tendencies for high-density points. It is advantageous to maintaining a relatively stable grinding force. The effectiveness of this method is verified through simulations and scanning/grinding experiments of different blades.

3-D Shape Matching of a Blade Surface in Robotic Grinding Applications

Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle.

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Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong

This letter proposes a novel algorithm for affine registration of point sets in the way of incorporating an affine transformation into the iterative closest point (ICP) algorithm. At each iterative step of this algorithm, a closed-form solution of the affine transformation is derived. Similar to the ICP algorithm, this new algorithm converges monotonically to a local minimum from any given initial parameters. To get the best affine registration result, good initial parameters are required which are successfully estimated by using independent component analysis (ICA). Experimental results demonstrate the robustness and high accuracy of this algorithm.

Affine Registration of Point Sets Using ICP and ICA

This paper presents an central difference Kalman filter (CDKF) based Simultaneous Localization and Mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent. To overcome the serious drawbacks of the previous frameworks, Sterling's polynomial interpolation method is employed to approximate nonlinear models. Combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem. The proposed approach improves the filter consistency and state estimation accuracy. Both simulated experiments and bench mark data set are used to demonstrating the superiority of the proposed algorithm.

A SLAM algorithm based on the central difference Kalman filter

Recently, genetic algorithm (GA) has been introduced as an effective method to solve the registration problem. It maintains a population of candidate solutions for the problem and evolves by iteratively applying a set of stochastic operators. Accordingly, a key question is how to reduce the population size. In this study, the authors present two techniques for reducing the population size in the GA for registration of partially overlapping point sets. Based on the trimmed iterative closest point algorithm, they introduce a growth operator into the GA. The growth operator, which is also inspired by the biological evolution, can improve the GA efficiency for registration. Furthermore, they present a technique called centre alignment to confirm the value range of all the registration parameters, which can reduce the search space and allow the well-designed GA to directly solve the registration problem. Experimental results carried out with the m-dimensional point sets illustrate its advantages over previous approaches.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2013.0545

Robust registration of partially overlapping point sets via genetic algorithm with growth operator

Global localization problem is one of the classical and important problems in mobile robot. In this paper, we present an approach to solve robot global localization in indoor environments with grid map. It combines Hough Scan Matching (HSM) and grid localization method to get the initial knowledge of robot's pose quickly. For pose tracking, a scan matching technique called Iterative Closest Point (ICP) is used to amend the robot motion model, this can drastically decreases the uncertainty about the robot's pose in prediction step. Then accurate proposal distribution taking into account recent observation is introduced into particle filters to recover the best estimate of robot trajectories, which seriously reduces number of particles for pose tracking. The proposed approach can globally localize mobile robot fast and accurately. Experiment results carried out with robot data in indoor environments demonstrates the effectiveness of the

https://journals.sagepub.com/doi/pdf/10.5772/10525

Zejian Yuan

Papers

Affine Registration of Point Sets Using ICP and ICA

A SLAM algorithm based on the central difference Kalman filter

Robust registration of partially overlapping point sets via genetic algorithm with growth operator

An Improved Technique for Robot Global Localization in Indoor Environments

Merging grid maps via point set registration