README.md

@@ -30,10 +30,9 @@ graph LR;
     A[VSLAM]-->|KeyFrames| B[YOLOv4];
     A-->|Pointcloud| C[Object Tracking];
     A-->|Camera Pose| C;
-    A-->|Pointcloud| E
     B-->|Object Detection|C;
-    C-->|Objects| D[Database];
-    D-->|Objects| E[GUI];
+    C-->|3D Objects| D[Database];
+    D-->|3D Objects| E[GUI];
 ```
 
 - **Camera/Video**: We use prerecorded videos as our examples and tests. We use two datasets, both augmented with depth perception data, which allows us to reconstruct a dense map for the scene captured from the footage. We also have a mechanism of collecting a monocular video feed (either from a drone or from regular phone camera) and we have Java scripts to break the footage into frames, which we can then perform monocular VSLAM on. Note that monocular VSLAM is not as effective as RGBD VSLAM, as we are unable to recreate the dense color map of the scene, therefore, we limit our functionality with this data source to simply demonstrating our ability to perform VSLAM on footage collected from an arbitrary camera source. 

src/main/java/vslam/README.md

-
-## Notes
-
-Notes from Rohan:
-- could use MATLAB coder to move MATLAB code to C/C++ code
-    - not sure if this is possible for Java code as well
-    - Prof. mentioned wanting Java code s.t. other students can read and critique codebase
-- use MATLAB functions, create new MATLAB objects for data structures / database system
-- dataset used by MATLAB example: https://cvg.cit.tum.de/data/datasets/rgbd-dataset 
-
-
-#### Performing VSLAM using online dataset 
-
-- Open up MATLAB console
-- Install necessary Mathworks Library: Vision, Visual SLAM (users will be queried to download necessary packages)
-- In MATLAB console, set imagesPath to 'rgbd_dataset_freiburg3_long_office_household/rgb'
-- Run the vslam_implementation.m script with the imagesPath as input
-- Use output of worldPointSet for figuring out which key features belong to which objects