Ahmad Shakaff Ali Yeon

Improving performance of 2D SLAM methods by complementing Kinect with laser scanner

The feasibility of using Kinect sensor for 2D Simultaneous Localization and Mapping (SLAM) application has been widely studied. Researchers concluded that the acquired maps are often inaccurate due to the limited field of view of the sensor. Therefore in this work, we complemented the Kinect with a laser scanner and proposed a method to merge the data from both sensors. Two SLAM algorithms (i.e Gmapping and Hector SLAM) were tested using the method, in different environments. The results show that the method is able to detect multi-sized objects and produce more accurate map as compared to when using single sensor (i.e Kinect only or laser scanner only). Finally, the performance of the Gmapping and Hector SLAM are compared particularly in terms of the computational complexity and the map accuracy.

Cross-sensitivity of metal oxide gas sensor to ambient temperature and humidity: Effects on gas distribution mapping

Metal oxide gas sensors have been widely used in robotics application to perform remote and mobile gas sensing. However, previous researches have indicated that this type of sensor technology is cross-sensitive to environmental temperature and humidity. This paper therefore investigates the effects of these two factors towards gas distribution mapping and gas source localization domains. A mobile robot equipped with TGS2600 gas sensor was deployed to build gas distribution maps of indoor environment, where the temperature and humidity varies. The results from the trials in environment with and without gas source indicated that there is a strong relation between the fluctuation of the mean and variance map with respect to the variations in the temperature and humidity maps.

Mobile robot localization system using multiple ceiling mounted cameras

Almost all robotics applications require accurate robot positioning. However, most of the developed methods lacks in ground truth reference to verify its accuracy relative to the real world. This paper proposes an effective vision based system to accurately track mobile robots true position and orientation using multiple overhead cameras. This system is able to track and localize multiple mobile robots simultaneously within a 3m × 6m arena. Images from the cameras are calibrated using calibration grid image to remove fish eye effect and further calibrated based on point coordinates (x, y) to eliminate camera angle distortion error. Each robot is assigned with a symbol marker for identification. A geometric feature based pattern matching algorithm is used to track the markers position and orientation. Data obtained from all four cameras are merged according to its relative offsets to obtain localization in a global coordinate frame. The developed system is able to localize multiple robots with errors of less than 1 cm and 1°.

Multi channel ultrasonic sensing system for wall features extraction

Ultrasonic sensor is one of the most cost-effective sensor used to obtain range information and obstacle avoidance. Due to its simplicity, this sensor is widely used in mobile robot applications to acquire environment features and mapping. Although the sensor can track a still or moving target, it does not provide information on the shape and pattern of the detected object. This paper proposes and highlights a low cost method using an array of ultrasonic sensors to be embedded on multiple robots for wall features extraction. Instead of using a single sensor, multiple sensors are used to increase the accuracy and improve coverage on the field of view of the sensor. More information can be extracted such as bearing angle of walls and possibly the shape of an object. A multiple pulse transmit and instantaneous multiple echo receive approach is implemented. The experimental results prove that this method is able to extract different type of wall features, accurately.

Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization

ABSTRACT Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM + V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5–1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation. GRAPHICAL ABSTRACT

Development of a Scalable Testbed for Mobile Olfaction Verification.

The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.