Virtual Experiments on Mobile Robot Localization with External Smart RGB-D Camera Using ROS

Automatic object searching is one of the essential skills for mobile robots to operate in unstructured and dynamical environments. It requires a robot to be endowed with object identification, obstacle avoidance, path planning, and navigation abilities. In this paper, a generic framework for automatic object searching is proposed. It has obstacle avoidance capability in unstructured and dynamical environments assisted by a behaviour learning algorithm using deep belief networks (DBN). As soon as a target object is recognized by a vision-based method, a bug-based path planning algorithm will be triggered for the robot in order to approach the target. Compared to state-of-art systems where laser sensors and cameras are widely used together for object identification, obstacle avoidance, path planning and navigation, only a single low cost RGB-D camera is used in our system to perform the above tasks. The proposed system has been implemented and tested in two indoor environments including a laboratory and a pantry of an office. The experimental results demonstrate the effectiveness of the proposed framework.

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Localization of an autonomous mobile robot has always been a topic of interest in Robotics community. The constraints of working under different environments are constantly a challenge in designing an efficient localization system. We propose a localization algorithm for a mobile robot, which will be used for home security. The objective is to perform indoor localization efficiently and keeping the cost as low as possible so as to make it commercializable. Here, we introduce an approach based on recognizing a pattern generated by laser pointers on the wall, which is captured by an infrared camera. The image of this pattern is used to locate the position of a robot in the room. The problem of working under black ceiling has been overcomed because the projection is on the wall and is moving which also increases the range of operation. As the pattern is unique, the interference of strong sunlight has also been fixed to some extent. Experimental results show that the proposed approach is effective in real environment and opens new doors for future research.

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

The paper studies the real-time stereo image-based localization of a vehicle in a prior 3D LiDAR map. A novel localization approach for mobile ground robot, which successfully combines conventional computer vision techniques, neural network based image analysis and numerical optimization, is proposed. It includes matching a noisy depth image and visible point cloud based on the modified Nelder-Mead optimization method. Deep neural network for image semantic segmentation is used to eliminate dynamic obstacles. The visible point cloud is extracted using a 3D mesh map representation. The proposed approach is evaluated on the KITTI dataset and a custom dataset collected from a ClearPath Husky mobile robot. It shows a stable absolute translation error of about 0.11 – 0.13 m. and a rotation error of 0.42 – 0.62 deg. The standard deviation of the obtained absolute metrics for our method is the smallest among other state-of-the-art approaches. Thus, our approach provides more stability in the estimated pose. It is achieved primarily through the use of multiple data frames during the optimization step and dynamic obstacles elimination on depth image. The method’s performance is demonstrated on different hardware platforms, including energy-efficient Nvidia Jetson Xavier AGX. With parallel code implementation, we achieve an input stereo image processing speed of 14 frames per second on Xavier AGX.

The paper proposes a method for localization of mobile robots by multiple landmark recognition landmarks are recognized in real time by model-based matching method using genetic algorithms (GA). A mobile robot measures directions of landmarks while moving. Target landmarks are changed depending on the situation of the robot. The individuals of GA are allocated for recognizing multiple landmarks efficiently depending on the situation. The robot estimates its position by the directions of the two landmarks and its moving distance. We perform experiments using a mobile robot in a RoboCup soccer field. In the experiment, goalposts and corner posts are used as landmarks. Experimental results show the effectiveness of the method.

Autonomous mobile robot field has gain interest among researchers in recent years. The ability of a mobile robot to locate its current position and surrounding environment is the fundamental in order for it to operate autonomously, which commonly known as localization. Localization of mobile robot are commonly affected by the inaccuracy of the sensors. These inaccuracies are caused by various factors which includes internal interferences of the sensor and external environment noises. In order to overcome these noises, a filtering method is required in order to improve the mobile robot’s localization. In this research, a 2- wheeled-drive (2WD) mobile robot will be used as platform. The odometers, inertial measurement unit (IMU), and ultrasonic sensors are used for data collection. Data collected is processed using Kalman filter to predict and correct the error from these sensors reading. The differential drive model and measurement model which estimates the environmental noises and predict a correction are used in this research. Based on the simulation and experimental results, the x, y and heading was corrected by converging the error to10 mm, 10 mm and 0.06 rad respectively.

The shortcomings of inertial sensor-based localization for mobile robots are discussed and a method to improve the performance of localization is introduced. Because dead reckoning using wheel speed sensors is insufficient for accurate motion estimation in uneven and slippery environments, many studies have used inertial sensors in order to improve the performance of localization. However, inertial sensor-based localization also has inherent problems caused by an accumulation of sensor errors. To improve the performance of inertial sensor-based localization for mobile robots, a method is suggested that involves the reduction of unexpected noise of inertial sensor using a Multiple Estimation Windows (MEW) filter. The main concept of the MEW filter is to estimate the long-term signal of interest using a single long-term moving window and multiple short-term moving windows. The proposed denoising method can reduce the noise of all frequencies without the loss of information from the signal of interest. The performance improvement of the mobile robot's localization using the MEW filter is confirmed experimentally.

High-precision indoor localization and tracking are essential requirements for the safe navigation and task execution of autonomous mobile robots. Despite the growing importance of mobile robots in various areas, achieving precise indoor localization remains challenging due to signal interference, multipath propagation, and complex indoor layouts. In this work, we present the first comprehensive study comparing the accuracy of Bluetooth low energy (BLE), WiFi, and ultra wideband (UWB) technologies for the indoor localization of mobile robots under various circumstances. In the performed experiments, the error margin of the WiFi-based systems reached 608.7 cm, which is not tolerable for most applications. As a commonly used technology in the existing tracking systems, the accuracy of BLE-based systems is at least 44.12% better than that of WiFi-based systems. The error margin of the BLE-based system in tracking static and mobile robots was 191.7 cm and 340.1 cm, respectively. The experiments showed that even with a limited number of UWB anchors, the system provides acceptable accuracy for tracking the mobile robots. Using only four UWB beacons in an environment of about 431 m2 area, the maximum error margin of detected positions by the UWB-based tracking system remained below 13.1 cm and 28.9 cm on average for the static and mobile robots, respectively. This error margin is 88.05% lower than that of the BLE-based system and 93.27% lower than that of the WiFi-based system on average. The high tracking precision, the need for a lower number of anchors, and the decreasing hardware costs point out that UWB will be the dominating technology in indoor tracking systems in the near future.

This paper presents a novel methods for estimating the position and orientation of a mobile robot using only sonar range data in an indoor environment. The mobile robot senses the environment with two sonar sensors as it moves. It utilizes the specular characteristic to recognize an edge and by referring to the location of the edge, the robot localizes itself rapidly. The data obtained by sensors at time t-1 can be used at time t, and are utilized to get the current position of a mobile robot. The presented algorithms are especially suitable for processing the sonar scan data obtained by ultrasonic sensors with wide beam spread. The validity of the proposed methods have been proved by experiments, and their performances are also investigated.

This paper presents a modified localization scheme of a mobile robot. When it navigates, the position error of a robot is increased and doesn't go to a goal point where the robot intends to go at the beginning. The objective of localization is to estimate the position of a robot precisely. Many algorithms were developed and still are being researched for localization of a mobile robot at present. Among them, a localization algorithm named continuous localization proposed by Schultz [2] has some merits on real-time navigation and is easy to be implemented compared to other localization schemes. Continuous Localization (CL) is based on map-matching algorithm with global and local maps using only ultrasonic sensors for making grid maps. However, CL has some problems in the process of searching the best-scored-map, when it is applied to a mobile robot. We here propose fast and powerful map-matching algorithm for localization of a mobile robot by experiments.

Simultaneous localization and mapping are techniques of mapping the homogeneous environments by localizing the sensors’ position in an environment with centimetre accuracy. This paper reviews the different techniques used in mapping and localization of mobile robot and designing of low-cost mobile platform with sensors like RPLIDAR and Microsoft Kinect. This paper also discussed the use of Robot Operating System and different packages and topics used for implementation of SLAM. Along with software implementation of ROS, this paper also covers the comparison of different hardware boards such as NVIDIA Jetson Nano and NVIDIA TK1 GPUs for running heavy and complex CUDA algorithms. Lower slave boards such as Arduino Mega and STM32 are also discussed in this paper. This paper also focuses on use of different localization techniques such as AMCL, ORB-SLAM, Hector SLAM, Gmapping, RTAB-MAP and particle filter SLAM. Robot Operating System played the crucial role in all the complex processing and communication between different running nodes. Autonomous navigation is achieved using ROS navigation stack, and simulations for the same are done in Rviz and gazebo real-time environments. 2D point cloud-based algorithms with laser scanner are compared against the 3D visualization techniques. At the end, clear comparison between all the benchmark algorithms such as Hector SLAM, Gmapping, RTAB-MAP, ORB-SLAM and ZEDfu is done for clear understanding while selecting an algorithm for future research.

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two‐dimensional LiDAR. Results show that reliable MCL that performs robust localization, self‐failure detection, and quick failure recovery can be realized.

Robust Monte Carlo localization for mobile robots