Mobile Robot Mapping Research Articles

Two different tools for three-dimensional mapping: DE-based scan matching and feature-based loop detection

SUMMARYAn autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into a map. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.

Mobile-robot pose estimation and environment mapping using an extended Kalman filter

In this paper an extended Kalman filter (EKF) is used in the simultaneous localisation and mapping (SLAM) of a four-wheeled mobile robot in an indoor environment. The robot’s pose and environment map are estimated from incremental encoders and from laser-range-finder (LRF) sensor readings. The map of the environment consists of line segments, which are estimated from the LRF’s scans. A good state convergence of the EKF is obtained using the proposed methods for the input- and output-noise covariance matrices’ estimation. The output-noise covariance matrix, consisting of the observed-line-features’ covariances, is estimated from the LRF’s measurements using the least-squares method. The experimental results from the localisation and SLAM experiments in the indoor environment show the applicability of the proposed approach. The main paper contribution is the improvement of the SLAM algorithm convergence due to the noise covariance matrices’ estimation.

SRCKF Based Simultaneous Localization and Mapping of Mobile Robots

In order to solve the large computing cost and numerical instabilities of simultaneous localization and mapping(SLAM),a square root cubature Kalman filter(SRCKF) based SLAM algorithm(SRCKF-SLAM) for mobile robots is designed according to cubature Kalman filter(CKF) principle.The SRCKF-SLAM algorithm accomplishes prediction and observation through motion model and observation model,and it is updated iteratively by propagating square root factors of the mean and covariance of the state variable,which guarantees the symmetry and positive semi-definiteness of the covariance matrix and therefore improves numerical accuracy and stability.The simulation experiments show that,compared with the CKF-SLAM algorithm,the root-mean square error of the SRCKF-SLAM algorithm decreases 14%,and the percentage of points in consistency area increases 36%,therefore the SRCKF-SLAM algorithm effectively satisfies the requirement of SLAM navigation of mobile robots.

An Improved Chaotic Motion Path Planner for Autonomous Mobile Robots Based on a Logistic Map

This paper presents a chaotic motion path planner based on a Logistic Map (SCLCP) for an autonomous mobile robot to cover an unknown terrain randomly, namely entirely, unpredictably and evenly. The path planner has been improved by arcsine and arccosine transformation. A motion path planner based only on the Logistic Chaotic Map (LCP) can show chaotic behaviour, which possesses the chaotic characteristics of topological transitivity and unpredictability, but lacks better evenness. Therefore, the arcsine and arccosine transformations are used to enhance the randomness of LCP. The randomness of the followed path planner, LCP, the improved path planner SCLCP and the commonly used Random Path Planner (RP) are discussed and compared under different sets of initial conditions and different iteration rounds. Simulation results confirm that a better evenness index of SCLCP can be obtained with regard to previous works.

1P1-I06 走行中における移動ロボットの実時間自己位置推定手法(移動ロボットの自己位置推定と地図構築(2))

1P1-I06 走行中における移動ロボットの実時間自己位置推定手法(移動ロボットの自己位置推定と地図構築(2))

1P1-H06 移動ロボットを用いたユーザの姿勢情報分類及びマッピング(移動ロボットの自己位置推定と地図構築(2))

1P1-H06 移動ロボットを用いたユーザの姿勢情報分類及びマッピング(移動ロボットの自己位置推定と地図構築(2))

1P1-H08 移動ロボットのためのランドマークの敷設環境調査と敷設ルール(移動ロボットの自己位置推定と地図構築(2))

1P1-H08 移動ロボットのためのランドマークの敷設環境調査と敷設ルール(移動ロボットの自己位置推定と地図構築(2))

A Set-Theoretic Algorithm for Real-Time Terrain Mapping of Mobile Robots in Outdoor Environments

In this paper, we address the problem of mapping outdoor rough terrain environments for mobile robots. While uncertainties arising from multiple sources are considered explicitly and assumed to be unknown-but-bounded, a set-theoretic framework is proposed to construct the terrain model as a set-valued elevation map that extends the notion of the elevation map with elevation variation in each cell stored by intervals. The localization problem of the mobile robot is also considered and solved by a set-membership filter in order to provide guaranteed bounded-pose estimation, which can be incorporated to the elevation map to improve the accuracy of the final terrain model. A more compact terrain representation can be obtained by the proposed algorithm with relatively low computational complexity, which makes it suitable for real-time applications. Furthermore, improved smoothness is achieved by the inherent conservativeness of the set-theoretic method without additional filtering or interpolation processes. Simulations as well as real-life experiments of a mobile robot operating in outdoor rough terrain environments with a 2D scanning laser rangefinder demonstrate the effectiveness and robustness of the proposed method.

Indoor Semantic Mapping for Mobile Robot

Indoor Semantic Mapping for Mobile Robot

A novel loop closure detection method in monocular SLAM

This paper proposes an image appearance-based method to deal with the loop closure detection problem of monocular simultaneous localization and mapping for mobile robots. A bag-of-visual words approach is presented for building an appearance-based scene model. Subsequently, a fuzzy $$K$$ -means method is proposed to build a visual vocabulary synchronously. Each image can be represented by a vector of weighted words. The similarity between images is evaluated by the scalar product between the weighted vectors. A Bayesian filter algorithm is applied to update the detection probability and an inverse image retrieval method is employed to eliminate the wrong loop closure results. The experimental results demonstrate the efficiency of our proposed method.

Information-theoretic compression of pose graphs for laser-based SLAM

In graph-based simultaneous localization and mapping (SLAM), the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow–Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph. Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping.

Spatial models for context-aware indoor navigation systems: A survey

This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions.

Novel Sonar Salient Feature Structure for Extended Kalman Filter-Based Simultaneous Localization and Mapping of Mobile Robots

Not all line or point features capable of being extracted by sonar sensors from a cluttered home environment are useful for simultaneous localization and mapping (SLAM) of a mobile robot. This is due to unfavorable conditions such as environmental ambiguity and sonar measurement uncertainty. We present a novel sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The key concept is to extract circle feature clouds on salient convex objects by sonar data association called convex saliency circling. The centroid of each circle cloud, called a sonar salient feature, is used as a natural landmark for EKF-based SLAM. By investigating the environmental inherent feature locality, cylindrical objects are augmented conveniently at the weak SLAM-able area as a natural supplementary saliency to achieve consistent SLAM performance. Experimental results demonstrate the validity and robustness of the proposed sonar salient feature structure for EKF-based SLAM.

구조광 영상기반 전방향 거리측정 시스템 개발

In this paper, a ranging system is proposed that is able to measure 360 degree omnidirectional distances to environment objects. The ranging system is based on the structured light imaging system with catadioptric omnidirectional mirror. In order to make the ranging system robust against environmental illumination, efficient structured light image processing algorithms are developed; sequential integration of difference images with modulated structured light and radial search based on Bresenham line drawing algorithm. A dedicated FPGA image processor is developed to speed up the overall image processing. Also the distance equation is derived in the omnidirectional imaging system with a hyperbolic mirror. It is expected that the omnidirectional ranging system is useful for mapping and localization of mobile robot. Experiments are carried out to verify the performance of the proposed ranging system.

Mobile robot map building from time-of-flight camera

A map building algorithm for mobile robots is introduced in this paper. The perceived environment is represented in a map containing in each cell a probability of presence of an object or part of an object. The environment is represented as a collection of modular occupancy grids which are added to the map as far as the mobile robot finds objects outside the existing grids. In this approach a time-of-flight (ToF) camera is exploited as a range sensor for mapping. Indeed, one of the areas where ToF sensors are adequate is in obstacle avoidance, because the detection region is not only horizontal but also vertical, allowing to detect obstacles with complex shapes. The main steps of the map building algorithm are extensively described in the paper. The results of testing the algorithm are considered in two different indoor environments.

Multi-Focal Vision and Gaze Control Improve Navigation Performance

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

Multi-focal Vision and Gaze Control Improve Navigation Performance

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

2A1-K06 俯瞰計測を用いた未知空間における車輪型移動ロボットの自律走行法(移動ロボットの自己位置推定と地図構築(1))

2A1-K06 俯瞰計測を用いた未知空間における車輪型移動ロボットの自律走行法(移動ロボットの自己位置推定と地図構築(1))

신뢰성 높은 위치 인식을 위하여 방향을 고려한 확률적 스캔 매칭 기법

The scan matching is widely used in localization and mapping of mobile robots. This paper presents a probabilistic scan matching method. To improve the performance of the scan matching, a direction of data point is incorporated into the scan matching. The direction of data point is calculated using the line fitted by the neighborhood data. Owing to the incorporation, the performance of the matching was improved. The number of iterations in the scan matching decreased, and the tolerance against a high rotation between scans increased. Based on real data of a laser range finder, experiments verified the performance of the proposed direction augmented probabilistic scan matching algorithm.

Adaptive feature extraction and mapping for mobile robots in unknown environment

This paper proposes a new approach for feature and line extraction use in mobile robot mapping with high accuracy, efficient speed and low complexity to determine line boundaries. This algorithm called modified split and merge (MSAM) is robust against measurement noises and demonstrates satisfactory results on different surfaces. The method is based on the least square method to fit a line to a series of uncertain points. Different least square criterion is investigated to choose the best one for line extraction. A novel approach is proposed here to adopt threshold base split and merge method on different surfaces. The approach is applied to NAJI2 rescue robot for simultaneously localizing and mapping the observation of its performance. The results of this study proved that it has a good real-time capability to integrate the information of laser scanner into the navigation algorithm of the mobile robot; however, the extracted lines are suitable for object base mapping approaches. Key words: Line extraction, mapping, modified split and merge, feature extraction, fitting.