An Improved Technique for Robot Global Localization in Indoor Environments

Robot odor source localization in indoor environments based on gradient adaptive extremum seeking search

The purpose of this paper is to provide a new method for solving the problem of the 3D source localization in indoor environments with weak airflow by the experimental study on the multi-robot method. For this purpose, we developed a 3D MRO (mobile robot olfaction) system consisting of three mobile robots. The gas sensor carried with each robot has a moving range of 0.5 m to 1.5 m on the Z-axis. A total of 60 experiments were conducted to validate and compare the standard whale optimization algorithm (SWOA) and the standard particle swarm optimization (SPSO) methods in a 7.65 m×4.1 m experimental area with two source heights (0.75 m and 1.05 m). For two source heights, the success rate and localization step of the SPSO method are 56.7% (17/30) and 31.4 steps, respectively. In addition, the success rate and localization step of the SWOA method are 80% (24/30) and 32.9 steps, respectively. These results show that the SWOA method has strong application potential in indoor environments with weak airflow.

Global and local localizations are critical for mobile robots to perform tasks without human support. In this study, we proposed a new method for global and local localizations that combines an environmental magnetic field with a scan matching method. In this method, we record the environmental magnetic field at each waypoint during the map-building process. To perform global localization, we rotate the mobile robot’s magnetic sensor-detected heading direction (current magnetic heading direction) to approximate the heading direction of the environmental magnetic field stored on the map. Subsequently, we use the scan matching method at positions around each waypoint. The mobile robot is currently in the position with the highest matching rate. The experimental results show that the mobile robot can successfully localize almost anywhere in the indoor environment, with the exception of a position where the magnetic sensor is affected by electric boards, and has a high success rate in an outdoor environment. For the local localization, we calculate the deviation between the mobile robot’s current magnetic heading direction and the one stored on the map, and then use this deviation to improve the localization using the scan matching method. The experimental results show that the mobile robot can perform much stronger localization during autonomous navigation.

This paper presents a new object entity based global localization approach with stereo camera. A local invariant feature and stereo depth information are used as visual features. The map we use here is a hybrid of global topological map and local object location map. The topological map includes some semantic information about the representing space and the object entities in the space. The object location map has the pose information of each object entity and visual features for object recognition. The localization process consists of two stages: coarse pose estimation and refined pose estimation. The coarse pose is computed by using the object recognition and point cloud fitting method. And the refined pose is estimated with particle filtering algorithm. An experiment shows that our approach can be an effective vision-based global localization method.

Abstract. The automation of unmanned vehicle operation has gained a lot of research attention, in the last few years, because of its numerous applications. The vehicle localization is more challenging in indoor environments where absolute positioning measurements (e.g. GPS) are typically unavailable. Laser range finders are among the most widely used sensors that help the unmanned vehicles to localize themselves in indoor environments. Typically, automatic real-time matching of the successive scans is performed either explicitly or implicitly by any localization approach that utilizes laser range finders. Many accustomed approaches such as Iterative Closest Point (ICP), Iterative Matching Range Point (IMRP), Iterative Dual Correspondence (IDC), and Polar Scan Matching (PSM) handles the scan matching problem in an iterative fashion which significantly affects the time consumption. Furthermore, the solution convergence is not guaranteed especially in cases of sharp maneuvers or fast movement. This paper proposes an automated real-time scan matching algorithm where the matching process is initialized using the detected corners. This initialization step aims to increase the convergence probability and to limit the number of iterations needed to reach convergence. The corner detection is preceded by line extraction from the laser scans. To evaluate the probability of line availability in indoor environments, various data sets, offered by different research groups, have been tested and the mean numbers of extracted lines per scan for these data sets are ranging from 4.10 to 8.86 lines of more than 7 points. The set of all intersections between extracted lines are detected as corners regardless of the physical intersection of these line segments in the scan. To account for the uncertainties of the detected corners, the covariance of the corners is estimated using the extracted lines variances. The detected corners are used to estimate the transformation parameters between the successive scan using least squares. These estimated transformation parameters are used to calculate an adjusted initialization for scan matching process. The presented method can be employed solely to match the successive scans and also can be used to aid other accustomed iterative methods to achieve more effective and faster converge. The performance and time consumption of the proposed approach is compared with ICP algorithm alone without initialization in different scenarios such as static period, fast straight movement, and sharp manoeuvers.

Position estimation based on scan matching is a type of popular approaches for mobile robot localization in indoor office-like environments. Global scan matching and local scan matching are two basic position estimation problems. Global scan matching is subject to global localization for mobile robot and local scan matching is used to deal with tracking mobile robot position over time. Global localization based on scan matching is represented in this paper. A global map is built to represent whole environment feature and a local map is built to represent current range scan. Global map and local map consist of a lot of line segments. Complete line segment that represents a complete environment object is singled out and used to match between local map and global map. Line segments are sorted counterclockwise in two maps. This type of ordinal map improves the searching efficiency of matching and localization algorithm is timesaving. In addition, matching between current range scan and global map is based on relative position relationships of line segments in local and global map. Global localization based on relative position relationship avoids frequent coordinates transformation and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.

Global localization methods deal with the estimation of a mobile robot's pose assuming no prior state information about it and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existent algorithms are based on the minimization of a L2-norm loss function. However, the use of a L1-norm offers some alternative advantages. The present work explores the use of a L1-norm together with an Evolutive Localization Filter to determine its efficiency when applied to the global localization problem. The algorithm has been tested subject to different noise levels to demonstrate the accuracy, effectiveness, robustness and computational efficiency of the L1-norm approach.

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

This paper presents a convenient and practical pose measurement method with satisfactory accuracy to obtain the absolute pose of the mobile robot near the destination in the wide-area and multi-destination scenario. The proposed method consists of the following distinguished features: first, an improved Monte-Carlo localization with the scan matching method is proposed to accurately calculate the absolute pose (target pose) of the mobile robot at the target destination; then, an eye-in-hand vision system is designed to attain the pose error between the actual and target pose of the robot, and the actual absolute pose can be obtained. At this point, the pose measurement near the destination is realized; moreover, these accurate measurement data are in application to parking error compensation. Compared to traditional measurement methods, our method does not require many additional devices to implement in the scene and meet the measurement requirements near the destination with guaranteed accuracy. The feasibility of the proposed method is verified through several experiments using the self-developed mobile robot.

This paper researches a robotic localization system based on motion capture indoor environment. We apply motion capture technology to realize multi-robot positioning which achieves the multi-robots’ localization in indoor environment. It includes motion capture system, some robots, and main computer for data calibration and communication. Motions capture system based on the theory of photogrammetry which was analyzed. And the virtual experiment environment was established according to the actual experimental environment. The performance is demonstrated by numerical simulation and experiment.

SummaryEstimating the robot state within a known map is an essential problem for mobile robot; it is also referred to “localization”. Even LiDAR-based localization is practical in many applications, it is difficult to achieve global localization with LiDAR only for its low-dimension feedback, especially in environments with repetitive geometric features. A sensor-fusion-based localization system is introduced in this paper, which has the capability of addressing the global localization problem. Both LiDAR and vision sensors are integrated, making use of the rich information introduced by vision sensor and the robustness from LiDAR. A hybrid grid-map is built for global localization, and a visual global descriptor is applied to speed up the localization convergence, combined with a pose refining pipeline for improving the localization accuracy. Also, a trigger mechanism is introduced to solve kidnapped problem and verify the relocalization result. The experiments under different conditions are designed to evaluate the performance of the proposed approach, as well as a comparison with the existing localization systems. According to the experimental results, our system is able to solve the global localization problem, and the sensor-fusion mechanism in our system has an improved performance.

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.

Two-dimensional (2D) laser point cloud matching of relative positioning plays an important role in Simultaneous Localization and Mapping (SLAM), which is the key component of mobile robots. By calculating the offset between the point sets of current profile and the original profile, relative positioning can be determined. With the rapid growth of data volume, faster and more effective scan matching methods should be published. This paper presented a novel scan matching method where 2D rotation was decoupled by transforming point cloud into Hough Domain (HD). The method based on HD and phase only matched filtering (POMF) performed faster matching speed and higher matching accuracy. POMF was adopted to register the overlapped result, and 2D transform was obtained by performing POMF on the x-axis and y-axis, respectively. Compared with the Iterative Closest Point (ICP), our method has 89.2% improvement in error and a 91.6% advance in the time when dealing with static data. Furthermore, the dynamic data processing experiment demonstrates a lower median and better consistent result.

Autonomous localization is a primary and crucial issue in mobile robot navigation tasks. In this article, the long-distance robust localization problem of indoor mobile robots is studied and solved by a combination style of using a laser scanner and an odometer. Firstly, a point-to-line iterative closest point(PLICP) approach is adopted to match the successive environmental information collected by a laser scanner to estimate the relative pose transformation of the robot. And then the multi-sensor fusion technology based on bounded-error set-membership estimator is proposed to to use scan matching results to correct the cumulative error of the odometer periodically to achieve precious location of the robot in indoor environments. Experimental results show that the accuracy and robustness of the proposed localization system has been improved greatly with respect to the single odometer localization approach.

New algorithm for footstep localization using seismic sensors in an indoor environment