Jongdae Jung

Indoor Mobile Robot Localization and Mapping Based on Ambient Magnetic Fields and Aiding Radio Sources

In robotics, the problem of concurrently addressing the localization and mapping is well defined as simultaneous localization and mapping (SLAM) problem. Since the SLAM procedure is usually recursive, maintaining a certain error bound on the current position estimate is a critical issue. However, when the robot is kidnapped (i.e., the robot is moved by an intentional or unintentional user) or suffers from locomotion failure (due to large slip and falling), the robot will inevitably lose its current position. In this case, immediate recovery of the robot position is essential for seamless operation. In this paper, we present a method of solving both SLAM and relocation problems by employing ambient magnetic and radio measurements. The proposed SLAM is realized in the Rao-Blackwellized particle filter- and grid-based SLAM frameworks, where we exploit the local heading corrections from the magnetic measurements. For the relocation, we design the location signatures using the magnetic and radio measurements, and examine each of the Monte Carlo localization-based and multilayer perceptron-based relocation methods with real-world data. We implement the proposed SLAM and relocation algorithms in an embedded system and verify the feasibility of the proposed methods as an online robot navigation system.

Indoor localization using particle filter and map-based NLOS ranging model

User localization is one of the key technologies for mobile robots to successfully interact with humans. Among various localization methods using radio frequency (RF) signals, time of arrival (TOA) based localization is popular since the target coordinates can be directly calculated from the accurate range measurements. In complex indoor environment, however, RF ranging-based localization is quite challenging since the range measurements suffer not only from signal noise but also from signal blockages and reflections. A set of range measurements taken in complex indoor environment verifies that almost all measurements are non-line-of-sight (NLOS) ranges which have striking difference to the line-of-sight (LOS) distances. These NLOS range measurements make severe degradation in the accuracy of trilateration based localizations if used without any compensation. In this paper we propose a particle filter-based localization algorithm which utilizes indoor geometry from a given map to estimate the NLOS signal path and compensates for the range measurements. The algorithm is verified with experiments performed in real indoor environments.

Magnetic field constraints and sequence-based matching for indoor pose graph SLAM

The objective of pose graph optimization is to estimate the robot trajectory from the constraints of relative pose measurements. Since the magnetic field in indoor environments is stable in the temporal domain and sufficiently varying in the spatial domain, we can exploit these characteristics to generate the constraints of the pose graph. In this paper we provide a method of solving a simultaneous localization and mapping (SLAM) problem by employing pose graph optimization and indoor magnetic measurements. Specifically, different types of constraints for local heading correction and global loop closing, respectively, are designed. For the loop closing constraints in particular, we first examine spatial similarity of the indoor magnetic field and verify that the use of measurement sequences rather than a single measurement mitigates the ambiguity of the magnetic measurements. A loop closing algorithm is then proposed based on the sequence of magnetic measurement and applied to the pose graph optimization. Experimental results show that the proposed SLAM system with only wheel encoders and a single magnetometer obtains comparable results with a reference-level SLAM system in terms of robot trajectory, thereby validating the feasibility of applying magnetic constraints to indoor pose graph SLAM. We design pose graph constraints using indoor magnetic field measurements.Magnetic measurements locally aid in the correction of heading directions.Use of a sequence of magnetic measurements aids in the detection of loop closures.Performance of a system with a single magnetometer and wheel encoders is evaluated.

Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.

Indoor user localization using particle filter and NLOS ranging model

User localization is one of the key technologies for mobile robots to successfully interact with humans. Among various localization methods using radio frequency (RF) signals, time of arrival (TOA) based localization is popular since the target coordinates can be directly calculated from the accurate range measurements. In complex indoor environment, however, RF-ranging-based localization is quite challenging since the range measurements suffer not only from signal noise but also from signal blockages and reflections. A set of range measurements taken in complex indoor environment verifies that almost all measurements are nonline-of-sight (NLOS) ranges which have striking difference to the line-of-sight (LOS) distances. These NLOS range measurements make severe degradation in the accuracy of trilateration based localizations if used without any compensation. In this paper we propose a particle filter based localization algorithm which exploits indoor geometry from the given map to estimate the NLOS signal path and compensate the range measurements. The algorithm is verified with experiments performed in real indoor environments.

Robotic SHM and Model-Based Positioning System for Monitoring and Construction Automation

As civil infrastructures get larger and more complex, the development of robot technologies that help to construct, monitor, inspect, and manage civil infrastructures is in need. In this paper, we introduce two major robot technologies for construction and monitoring of civil infrastructures. The first one is the beacon-based localization technology for the robust localization of the robot and human. A novel beacon-based localization method is proposed where it utilizes the map information of the structure and the signal propagation model. The second one is the modular robot system for structural health monitoring (SHM) of large structures. To develop an SHM system that directly measures the deformation of the structure using low-cost sensor, a paired structured light (SL)-based modular robot system is proposed. The proposed module which uses one or two actuated lasers and a camera in pair is inexpensive to implement and it can directly measure the accurate relative deformation between any two locations on the structure. To demonstrate the feasibility and capability of the proposed methods, experimental results have been shown for each technology.

Localization of AUVs using visual information of underwater structures and artificial landmarks

Autonomous underwater vehicles (AUVs) can perform flexible operations in complex underwater environments due to their autonomy. Localization is one of the key components of autonomous navigation. Since the inertial navigation system of an AUV suffers from drift, observing fixed objects in an inertial reference system can enhance the localization performance. In this paper, we propose a method of localizing AUVs by exploiting visual measurements of underwater structures and artificial landmarks. In a framework of particle filtering, a camera measurement model that emulates the camera’s observation of underwater structures is designed. The particle weight is then updated based on the extracted visual information of the underwater structures. Detected artificial landmarks are also used in the particle weight update. The proposed method is validated by experiments performed in a structured basin environment.

DV-SLAM (Dual-Sensor-Based Vector-Field SLAM) and Observability Analysis

In this paper, the observability of the conventional vector field simultaneous localization and mapping (SLAM) is examined by using the Fisher information matrix (FIM). If a mobile robot integrates sensor measurements while moving with a fixed heading, the measurements will be ambiguous because its measurement model is based on bilinear interpolation. To resolve the ambiguity, the authors proposed the novel dual-sensor-based vector-field SLAM (DV-SLAM), which is fully observable by using a mobile robot equipped with two sensors in a specific location to measure vector field signals. By examining its FIM, the condition is derived for the proposed DV-SLAM to be fully observable regardless of how the robot moves. The proposed DV-SLAM is implemented based on the Rao-Blackwellized particle filter with Earths magnetic field sensors. Simulation and experimental results demonstrate that the proposed dual-sensor-based approach greatly improves the performance of the vector-field SLAM compared with the conventional approach.

AUV localization using visual information of underwater structures

In this paper, we propose a method of AUV localization using visual measurement of underwater structures. Since the inertial navigation system (INS) of AUV suffers from drift, observing fixed objects can enhance the localization performance. The proposed method is validated by experiments performed in a structured basin environment.

Range-based indoor user localization using reflected signal path model

User localization is one of the key technologies for mobile robots to successfully interact with humans. Among various localization methods using radio frequency (RF) signals, time of arrival (TOA) based localization is popular since the target coordinates can be directly calculated from the accurate range measurements. In complex indoor environment, however, RF ranging-based localization is quite challenging since the range measurements suffer not only from signal noise but also from signal blockages and reflections. A set of range measurements taken in complex indoor environment verifies that almost all measurements are non-line-of-sight (NLOS) ranges which have striking difference to the line-of-sight (LOS) distances. These NLOS range measurements make severe degradation in the accuracy of trilateration based localizations if used without any compensation. In this paper we propose a particle filter-based localization algorithm which utilizes indoor geometry from a given map to estimate the NLOS signal path and compensates for the range measurements. The algorithm is verified with experiments performed in real indoor environments.