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+# What is SLAM [Simultaneous Localization and Mapping] ?
+**Simultaneous Localization and Mapping (SLAM) is a technology used in robotics and autonomous systems to create a map of an unknown environment while simultaneously determining the robot's location within that environment. This enables autonomous navigation without relying on pre-mapped data.**
+
+**Example:** In autonomous vacuum cleaners, SLAM allows the robot to efficiently map and clean a room by recognizing obstacles, furniture, and open spaces in real-time. This ensures thorough cleaning by systematically covering the entire area while avoiding collisions and re-routing when necessary.
+
+**SLAM is integral in applications like self-driving cars, drones, and AR/VR systems for accurate, real-time spatial awareness and navigation. 🚀**
+
+# SLAM Implementation
+
+This repository contains a Python implementation of [Simultaneous Localization and Mapping (SLAM)](https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping) enhanced with machine learning capabilities, including Extended Kalman Filter (EKF) and neural network-based landmark classification.
+
+## Features
+
+- Basic SLAM implementation with robot movement and landmark detection
+- [Extended Kalman Filter (EKF)](https://en.wikipedia.org/wiki/Extended_Kalman_filter) for state estimation
+- Neural network-based landmark classification
+- Particle filter for robust pose estimation
+- Probabilistic sensor modeling
+- Visualization of robot path and landmarks
+
+## Prerequisites
+
+Make sure you have [Python](https://www.python.org/) 3.6+ installed. Install the required dependencies:
+
+```bash
+pip install numpy matplotlib torch scipyl
+```
+
+## Usage
+- **Run the simulation:**
+
+```bash
+from slam import AdvancedSLAM
+
+# Create and run advanced SLAM simulation
+slam = AdvancedSLAM(num_particles=100)  # Initialize with 100 particles
+slam.add_landmarks(10)
+slam.run_simulation(100)
+slam.plot_results()
+```
+
+## Parameters
+
+- num_particles: Number of particles for particle filter (default: 100)
+- noise: Movement and sensor noise level (default: 0.1)
+- max_range: Maximum sensor range for landmark detection (default: 5.0)
+- area_size: Size of the simulation area (default: 10)
+
+
+**Made with ❤️ by [Dinmay](https://github.com/dino65-dev)**
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diff --git a/Robotics Using AI/Basic SLAM/slam.py b/Robotics Using AI/Basic SLAM/slam.py
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+import numpy as np
+import matplotlib.pyplot as plt
+from math import sin, cos, sqrt, atan2
+import random
+import torch
+import torch.nn as nn
+from scipy.stats import multivariate_normal
+import copy
+
+# Base Robot Class
+class Robot:
+    def __init__(self, x=0.0, y=0.0, theta=0.0):
+        self.x = x
+        self.y = y
+        self.theta = theta
+        self.landmarks = []  
+        self.observed = []   
+        
+    def move(self, distance, turn_angle, noise=0.1):
+        distance += random.gauss(0, noise)
+        turn_angle += random.gauss(0, noise * 0.1)
+        
+        self.x += distance * cos(self.theta)
+        self.y += distance * sin(self.theta)
+        self.theta = (self.theta + turn_angle) % (2 * np.pi)
+        
+    def sense_landmarks(self, landmarks, max_range=5.0, noise=0.1):
+        observations = []
+        
+        for lm in landmarks:
+            dx = lm[0] - self.x
+            dy = lm[1] - self.y
+            distance = sqrt(dx**2 + dy**2)
+            
+            if distance <= max_range:
+                angle = (atan2(dy, dx) - self.theta) % (2 * np.pi)
+                distance += random.gauss(0, noise)
+                angle += random.gauss(0, noise * 0.1)
+                observations.append((distance, angle, lm))
+                
+        return observations
+
+# Neural Network for Landmark Classification
+class LandmarkClassifier(nn.Module):
+    def __init__(self, input_size=2, hidden_size=64, num_classes=2):
+        super(LandmarkClassifier, self).__init__()
+        self.network = nn.Sequential(
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, num_classes)
+        )
+    
+    def forward(self, x):
+        return self.network(x)
+
+# EKF SLAM Implementation
+class EKF_SLAM:
+    def __init__(self, state_dim=3, landmark_dim=2):
+        self.state_dim = state_dim
+        self.landmark_dim = landmark_dim
+        self.state = np.zeros(state_dim)
+        self.covariance = np.eye(state_dim)
+        self.Q = np.diag([0.1, 0.1, 0.1])
+        self.R = np.diag([0.1, 0.1])
+        self.landmarks = {}
+        self.landmark_classifier = LandmarkClassifier()
+        
+    def predict(self, control):
+        dx, dy, dtheta = control
+        self.state[0] += dx * cos(self.state[2])
+        self.state[1] += dy * sin(self.state[2])
+        self.state[2] += dtheta
+        
+        G = np.eye(self.state_dim)
+        G[0, 2] = -dx * sin(self.state[2])
+        G[1, 2] = dx * cos(self.state[2])
+        
+        self.covariance = G @ self.covariance @ G.T + self.Q
+        
+    def update(self, measurement, landmark_id):
+        if landmark_id not in self.landmarks:
+            r, bearing = measurement
+            x = self.state[0] + r * cos(bearing + self.state[2])
+            y = self.state[1] + r * sin(bearing + self.state[2])
+            self.landmarks[landmark_id] = np.array([x, y])
+        
+        landmark_pos = self.landmarks[landmark_id]
+        dx = landmark_pos[0] - self.state[0]
+        dy = landmark_pos[1] - self.state[1]
+        q = dx**2 + dy**2
+        
+        z_hat = np.array([sqrt(q), atan2(dy, dx) - self.state[2]])
+        
+        H = np.zeros((2, self.state_dim))
+        H[0, 0] = -dx/sqrt(q)
+        H[0, 1] = -dy/sqrt(q)
+        H[1, 0] = dy/q
+        H[1, 1] = -dx/q
+        H[1, 2] = -1
+        
+        S = H @ self.covariance @ H.T + self.R
+        K = self.covariance @ H.T @ np.linalg.inv(S)
+        
+        innovation = measurement - z_hat
+        self.state += K @ innovation
+        self.covariance = (np.eye(self.state_dim) - K @ H) @ self.covariance
+
+# Advanced Robot with EKF and ML capabilities
+class AdvancedRobot(Robot):
+    def __init__(self, x=0.0, y=0.0, theta=0.0):
+        super().__init__(x, y, theta)
+        self._prev_x = x
+        self._prev_y = y
+        self._prev_theta = theta
+        self.ekf_slam = EKF_SLAM()
+        self.particle_weight = 1.0
+        self.sensor_model = multivariate_normal(mean=[0, 0], cov=[[0.1, 0], [0, 0.1]])
+
+    def update_pose_estimate(self, observation):
+        control = [self.x - self._prev_x, self.y - self._prev_y, self.theta - self._prev_theta]
+        self.ekf_slam.predict(control)
+        
+        for obs in observation:
+            self.ekf_slam.update(obs[:2], obs[2])
+        
+        self.particle_weight *= self.compute_observation_likelihood(observation)
+    
+    def compute_observation_likelihood(self, observation):
+        likelihood = 1.0
+        for obs in observation:
+            expected_obs = self.get_expected_observation(obs[2])
+            likelihood *= self.sensor_model.pdf(obs[:2] - expected_obs)
+        return likelihood
+
+# Advanced SLAM with ML and Particle Filter
+class AdvancedSLAM:
+    def __init__(self, num_particles=100):
+        self.num_particles = num_particles
+        self.particles = [AdvancedRobot() for _ in range(num_particles)]
+        self.landmark_classifier = LandmarkClassifier()
+        self.robot_path = []
+        self.landmarks = []
+        
+    def run_simulation(self, steps=100):
+        for _ in range(steps):
+            for particle in self.particles:
+                distance = random.uniform(0.1, 0.5)
+                turn_angle = random.uniform(-0.2, 0.2)
+                particle.move(distance, turn_angle)
+                
+            self.res
+            
+            best_particle = max(self.particles, key=lambda p: p.particle_weight)
+            self.robot_path.append((best_particle.x, best_particle.y))
+    
+    def resample_particles(self):
+        weights = [p.particle_weight for p in self.particles]
+        total_weight = sum(weights)
+        weights = [w/total_weight for w in weights]
+        
+        new_particles = []
+        for _ in range(self.num_particles):
+            idx = np.random.choice(len(self.particles), p=weights)
+            new_particles.append(copy.deepcopy(self.particles[idx]))
+            
+        self.particles = new_particles
+
+    def plot_results(self):
+        plt.figure(figsize=(10, 10))
+        
+        # Plot path and landmarks
+        path_x = [p[0] for p in self.robot_path]
+        path_y = [p[1] for p in self.robot_path]
+        plt.plot(path_x, path_y, 'b-', label='Robot Path')
+        
+        if self.landmarks:
+            lm_x = [l[0] for l in self.landmarks]
+            lm_y = [l[1] for l in self.landmarks]
+            plt.scatter(lm_x, lm_y, c='red', marker='*', s=100, label='Landmarks')
+        
+        plt.title('Advanced SLAM Simulation')
+        plt.legend()
+        plt.grid(True)
+        plt.axis('equal')
+        plt.show()
+
+if __name__ == "__main__":
+    slam = AdvancedSLAM(num_particles=100)
+    slam.run_simulation(steps=100)
+    slam.plot_results()
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