## 5 Results and Discussion  
In this section, a series of simulations that include laser measurements from the Turtlebot3 platform is performed to validate our proposed online detection approach. A unit of TurtleBot3 (see in Figure 4) consisting of a two-wheeled platform where its weight and size are 1.8 kg and 281 \(\times\) 306 \(\times\) 141 mm, respectively, is selected.  
The mobile robot runs with a use single-board computer with the latest version of Ubuntu Linux (16.04.2 LTS) and Robot Operating System (ROS) Kinetic. The development of ROS packages is still ongoing where in [30], researchers proposed a ROS wrapper of Real Time Appearance Based Mapping with the help of a low cost camera and the Microsoft Kinect XBOX 360 mounted on Turtlebot to improvise the indoor navigation. Moreover, Adaptive Monte Carlo Localization was used for localizing the Turtlebot model in the map. Recent work by [PERSON] et al. proposed a ROS framework for development and testing of autonomous control functions. The flexibility and power of ROS was combined with the Robotic Systems toolbox from MATLAB/Simulink, Linux embedded systems and a commercially available autopilot. Thus, it results in a low cost, simple, highly flexible, and reconfigurable system [31].  
A 360\({}^{\circ}\)LiDAR_node ROS package can be added easily to read and transmit the laser measurements to the remote processor. The use of LiDAR sensor Turtlebot3 can be accessed and commanded from ROS using the mavros package. This TurtleBot model also has a Raspberry Pi Camera and multiple sensors, including gyroscopic, magnetometer, and accelerometer. Note that the system model platform which is running on Gazebo environment is very close to the actual experimental [32], and the multimodal components in the simulated environment can reflect the characteristics of the actual components in a more comprehensive way.  
It was compared with three benchmarks, namely Maximum Current Weight (MCW), Measurement Entropy (ME), and Metric-based Detector against QS-2 DDTW with the same test as in Algorithm 2. Two types of test were performed which were \"Test with re-localization\" and \"Test without re-localization\". The purpose of this test division was to see how dependent the accuracy of \(\mathcal{M}\) was to the relocalization process which was started at \(t=t\_k+1\). For this purpose, the evaluator in (2) was evaluated for all time instances \(2\leq t\leq T\) instead of \(2\leq t\leq t\_k-1\). White Gaussian noise with variance 0.1 was employed in the measurement model in each simulation. Since the Gaussian noise model was used, Bartlett test for the variance test was employed for the proposed method. The kidnapping scenario is depicted in Figure 5. This kidnapping scenario can be explained more explicitly, compactified from Algorithm 2, as below:  
For all \(2\leq t\_k\leq 100\), perform the following procedure:  
1. Let the robot run normally. At \(t=t\_k\) the robot is kidnapped to a place indicated in Figure 5 and after that it continues to run normally until \(t=100\) (at \(t=t\_k+1\) the robot can be relocalized or not, depending on the type of test)
2. See if the robot can detect that it has been kidnapped at \(t=t\_k\).  
Figure 4: TurtleBot3 Waffle platform (**left**) and its navigation through an indoor area (**right**).  
* Repeat this 100 times and measure the performance index.