## 3 Proposed Work
### State Space Model
Compute the neighborhood similarity (The quantity of neighbors lessens with time)
5. Reward the winning weight by resembling the sample vector (neighbors resemble the sample vector)
6. Repeat the step b for N times End for
6. Allocate Color Index (CI) weight to every clustered point
7. Perform PC registration for successive frames
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**Algorithm 3**Pre-learning. In this work, physical ground truth values are employed. The unlabeled data are first introduced into the workspace, then labels are assigned to the designated objects and moved to the workspace. Label assignment is performed based on point similarity using the Color Index (CI). The CI is an RGB-based vector with values ranging from 0 to 255. These elements are deemed crucial as LiDAR sensor manufacturers assign labels, which are a collection of CI-related values. Using a bounding box that represents an object from an unstructured smart environment, these inputs are used to assign labels. The label \"1 to N\" is assigned with a CI value of \"255, 255, 255\". The PC's image frames are read to register the arrangement of images, and the Normal Distributions Transform (NDT) scheme is used for image registration. The successive image frames of the PC are combined, resulting in a two-dimensional mono-model with labels. The coordinate points and labels are included as features in the vector representation. Figure 3: Workflow of Pre-Learning. ## 4 SS System

Segmentation involves dividing the points of a PC into small, understandable, and connected subsets. It is a divisive approach. The points are then labeled based on their similarity, and each subset of points has distinctive characteristics. The PC process involves three pipelined processes. Figure 4 shows the proposed architecture of the SS system. Understanding semantics is a crucial aspect in autonomous cars and reverse engineering and is a part of the positioning and vision-dependent navigation model for independent robotic systems. Segmentations are of different types. The local segmentation of PCs categorizes data points based on geometric factors in cluster-based schemes. Graph-dependent segmentation schemes are motivated by the neighborhood graph structure. However, these schemes have limitations such as difficulties in identifying borders and interpolation in the case of similar closed objects. To address these problems, the researchers have introduced a local descriptor that can be used to extract the relevant features and information from the dataset. The Fast Point Fast Histogram (FPFH) [40] scheme is utilized to draw out local features. ### Feature Extraction

There have been various proposals on how to extract discriminative features from PCs that have varying densities of points [41]. Spin image descriptors are formed by rotating a, and can be used to characterize the local 3D area surrounding a point X. An effective alternative to spin image descriptors is the use of shape distributions, which involve randomly sampling geometric region properties such as intensity, color, texture, contours, regions, angles, and lengths. To determine the parameters of a color transformation that best separates the two groups, color data from the vehicle and nonroad zones are employed. After that, a line fitting method is used to express the contour as a series of parallel lines. Calculating the distances from the vehicle at each place \(x_{i}\), where the range is \(d_{i,j}=\parallel x_{i}\parallel\) for point \(i^{th}\) allows us to describe the texture of an area. To eliminate the mean and magnify the noise, the distance data are next treated by obtaining the exact number of the first derivatives of the data. As a result, they are suitable for describing the immediate vicinity of a 3D point X [42]. Point Feature Histograms (PFHs) is another method that follows a similar approach. These relations are then encoded into histograms to construct the PFHs [43].