Finding Memory leaking, Stack and Heap overflow

When you access an array index, C and C++ don’t do bound checking. Segmentation faults only happen when you try to read or write to a page that was not allocated (or try to do something on a page which isn’t permitted, e.g. trying to write to a read-only page), but since pages are usually pretty big (multiples of a few kilobytes), it often leaves you with lots of room to overflow.

If your array is on the stack, it can be even worse as the stack is usually pretty large (up to several megabytes). This is also the cause of security concerns: writing past the bounds of an array on the stack may overwrite the return address of the function
and lead to arbitrary code execution (the famous “buffer overflow” security breaches).

By setting the following flags you can find the issue:

Example:

References: [1]

Extended Kalman Filter Explained with Python Code

In the following code, I have implemented an Extended Kalman Filter for modeling the movement of a car with constant turn rate and velocity. The code is mainly based on this work (I did some bug fixing and some adaptation such that the code runs similar to the Kalman filter that I have earlier implemented).

Extended Kalman filter

Trajectory of the car, click on the image for large scale



References: [1] [2] [3] [4] [5]

Parcticle Filter Explained With Python Code From Scratch

In the following code I have implemented a localization algorithm based on particle filter.

I have used conda to run my code, you can run the following for installation of dependencies:

and the code:

 

Kalman Filter Explained With Python Code From Scratch

This snippet shows tracking mouse cursor with Python code from scratch and comparing the result with OpenCV. The CSV file that has been used are being created with below c++ code. A sample could be downloaded from here 1, 2, 3.

Python Kalman Filter

C++ and OpenCV Kalman Filter

Rapidcsv has been downloaded from here

 

Eigen unaryExpr (Function Pointer, Lambda Expression) Example

 

Matrix Decomposition with Eigen: QR, Cholesky Decomposition LU, UL

 

 

Eigen Memory Mapping

 

 

Eigen Arrays, Matrices and Vectors: Definition, Initialization Resizing, Populating and Coefficient Wise Operations

 

 

Computing Essential and Fundamental Matrix Using OpenCV (8 Points Algorithm) With C++

 

Decomposing Projection Using OpenCV and C++

 

 

And the output is: