**Acknowledgement**

518 MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications – Volume 1

together with this craters detection algorithm

noble future work for new researchers.

Baizura Bohari and Noorlina Zainuddin *National Defense University of Malaysia, Malaysia* 

Nur Diyana Kamarudin, Kamaruddin Abd. Ghani, Siti Noormiza Makhtar,

**Author details** 

patch. This output will then be used in ellipse reconstruction algorithm in order to get the

There are some limitations that have to be stated here for further extension and modification. For the craters detection algorithm, it is dependent on the sun angles and these assumptions will lead to an error of detecting a true pair (light and dark pairing patch). In addition, there are uncertainties as discussed before from the software (MATLAB) and the hardware itself in a real application (altimeter to measure the altitude). The Hough method seems to give more precise results but have a constraint in the shape of a crater itself. For an instance, the reconstruction needs to analyze a crater as an ellipse model instead of a circle. In Hough ellipse transformation, the authors have to analyze the ellipse in 5 dimensions instead of 3 dimensions in a circle. These limitations make the Hough Transform method to be unreliable and make its computational method a burden to use

For future works, this useful research can be extended to a crater pattern matching as described in the Literature Review section above. Craters Pattern matching is proposed by previous researchers to attain the position and velocity estimation of a spacecraft and a Lander during Entry, Descent and Landing (EDL) purposes and also for autonomous precision landing purposes. By making a pattern matching, one can get the differentiation between the position determined by the pattern matching and those from the reconstruction algorithm. The errors in the crater's position between these two methods can be evaluated to determine which is better in a real application. In reality, the lunar's surface is not flat and the camera parameters will not usually estimate perfectly. The image does require scaling, but the true amount is impossible to be identified without also knowing the camera's specifications (focal length and field of view). In most cases, the picture is not usually taken straight at the centre of the image and perspective distortion will have an effect as discussed before. As none of these are true in real applications, the need of the reconstruction algorithm to find the position of the crater is high. The crater's position determination and evaluation of this reconstruction algorithm were discussed in detail in the previous section. Then, the authors can determine the velocity of the spacecraft based on the position and the orientation of the crater. The idea is, if the authors can find the position and orientation in a single frame, then the velocities are the difference from one frame to the other one. Therefore, this research has a great valuable for future works. In addition, this research is a very worthy research indeed and has valuable benefits to any spacecraft missions in order to avoid the hazardous craters (feature proposed) and for a moon Lander to have a precise landing on a Lunar. Besides, the authors can compare the position determined using equation *q* and the position determined using the craters pattern matching and this will be a

orientation, *p* and further position, *q* of the disc (crater) from the camera's projection.

We would like to express our gratitude to our supervisors Dr Phil Palmer and Dr David Wokes from the University of Surrey, United Kingdom for their guidance and support. This academic article is also dedicated to our new supervisor, Associate Professor Major (R) Ir. Kamaruddin Abd. Ghani, co-supervisors, our beloved families and university. This chapter is fully supported by the Department of Electrical and Electronic Engineering, Universiti Pertahanan Nasional Malaysia, Malaysia.
