Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

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Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter.

This paper presents an activity sequence-based indoor pedestrian localization approach using smartphones. The activity sequence consists of several continuous activities during the walking process, such as turning at a corner, taking the elevator, taking the escalator, and walking stairs. These activities take place when a user walks at some special points in the building, like corners, elevators, escalators, and stairs. The special points form an indoor road network. In our approach, we first detect the user's activities using the built-in sensors in a smartphone. The detected activities constitute the activity sequence. Meanwhile, the user's trajectory is reckoned by Pedestrian Dead Reckoning (PDR). Based on the detected activity sequence and reckoned trajectory, we realize pedestrian localization by matching them to the indoor road network using a Hidden Markov Model. After encountering several special points, the location of the user would converge on the true one. We evaluate our proposed approach using smartphones in two buildings: an office building and a shopping mall. The results show that the proposed approach can realize autonomous pedestrian localization even without knowing the initial point in the environments. The mean offline localization error is about 1.3 m. The results also demonstrate that the proposed approach is robust to activity detection error and PDR estimation error.

Activity Sequence-Based Indoor Pedestrian Localization Using Smartphones

To improve our understanding of the indoor properties of the perturbed Earth's magnetic field, we have developed a methodology to obtain dense and spatially referenced samples of the magnetic vector field on the ground's surface and in the free space above. This methodology draws on the use of various tracking techniques (photometric, odometric, and motion capture) to accurately determine the pose of the magnetic sensor, which can be positioned manually by humans or autonomously by robots to acquire densely gridded sample datasets. We show that the indoor magnetic field exhibits a fine-grained and persistent micro-structure of perturbations in terms of its direction and intensity. Instead of being a hindrance to indoor navigation, we believe that the variations of the three vector components are sufficiently expressive to form re-recognizable features based on which accurate localization is possible. We provide experimental results using our methodology to map the magnetic field on the ground's surface in our indoor research facilities. With the use of a magnetometer and very little computation, these resulting maps can serve to compensate the perturbations and subsequently determine pose of a human or robot in dead reckoning applications.

Characterization of the indoor magnetic field for applications in Localization and Mapping

The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed, and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling-based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, the characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.

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Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step ��� the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth���s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically determine when sensed magnetic heading is accurate enough to be useful for additional error compensation.

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors [Emerging Opportunities for Localization and Tracking]

A navigation sensor integration architecture is proposed to improve navigational reliability and accuracy on vessels having two inertial navigators plus other aiding sensors, such as GPS, Loran-C, Omega, and speed log. This hierarchic alliance of Kalman filters is a systematic structure of dynamically configured parallel filters, sharing measurements but not state information. Unlike a centralized, or even a federated architecture, this scheme can always provide an uncorrupted optimal solution, even after the occurrence of a subtle sensor or software fault that was not immediately detected. Thus it substantially simplifies reconfiguration and eliminates the need to “back out” of a failure. It also facilitates the detection and isolation of sensor and software faults by allowing an additional level of tests, which use sensitive intrafilter fault detection techniques with interfilter fault isolation. These techniques are investigated using high-fidelity simulations and sea trial data, with encouraging results.

Dale Arden

Papers

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors [Emerging Opportunities for Localization and Tracking]

Techniques for soft-failure detection in a multisensor integrated system

A Highly Fault Tolerant, Dual Inertial Integrated Navigation System (DIINS)