diff --git a/pass_on_left_ego_only.py b/pass_on_left_ego_only.py
new file mode 100644
index 0000000..9217215
--- /dev/null
+++ b/pass_on_left_ego_only.py
@@ -0,0 +1,28 @@
+import SG_Primitives as P
+from SymbolicEntity import SymbolicEntity
+from SymbolicProperty import SymbolicProperty
+from functools import partial
+
+EGO = partial(P.filterByAttr, "G", "name", "ego")
+HUMAN = SymbolicEntity('person_1', ['person'])
+
+def person_in_direction(robot, person, direction_label):
+    """
+    Checks if the person is located in a specific direction relative to the robot.
+    """
+    direction_set = partial(P.relSet, robot, direction_label)
+    overlapping_entities = partial(P.intersection, direction_set, person)
+    return partial(P.gt, partial(P.size, overlapping_entities), 0)
+
+
+passing_human_on_left = SymbolicProperty(
+    "robot_must_pass_human_on_the_left",
+    "((is_front & !is_behind) -> (!is_left U is_behind))",
+    [
+        ("is_front", person_in_direction(EGO, HUMAN, "FRONT")),
+        ("is_left",  person_in_direction(EGO, HUMAN, "LEFT")),
+        ("is_behind", person_in_direction(EGO, HUMAN, "BACK"))
+    ],
+    [HUMAN]
+)
+all_human_properties = [passing_human_on_left]
diff --git a/ros2_async_tracker.py b/ros2_async_tracker.py
new file mode 100644
index 0000000..7753699
--- /dev/null
+++ b/ros2_async_tracker.py
@@ -0,0 +1,318 @@
+import rclpy
+from rclpy.node import Node
+from rclpy.callback_groups import MutuallyExclusiveCallbackGroup
+from rclpy.executors import MultiThreadedExecutor
+from sensor_msgs.msg import CompressedImage
+from geometry_msgs.msg import PoseArray
+from nav_msgs.msg import Odometry  # NEW: We need to import the Odometry message type
+import torch
+from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights
+from PIL import Image as PILImage
+import torchvision.transforms as T
+import cv2
+import numpy as np
+import math
+import threading
+import networkx as nx
+from pass_on_left_ego_only import all_human_properties
+from geometry_msgs.msg import PoseArray
+from visualization_msgs.msg import Marker
+
+class SceneNode:
+    def __init__(self, node_id, name, base_class):
+        self.id = node_id
+        self.name = name
+        self.base_class = base_class
+        
+    def get_id(self): return self.id
+    def is_phantom(self): return False
+    def __hash__(self): return hash(self.id)
+    def __eq__(self, other): return getattr(other, 'id', None) == self.id
+
+class AsyncTrackingNode(Node):
+    def __init__(self):
+        super().__init__('async_tracking_node')
+
+        self.lock = threading.Lock()
+
+        self.get_logger().info('Loading PyTorch model...')
+        weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
+        self.model = fasterrcnn_resnet50_fpn(weights=weights)
+        self.model.eval()
+        self.transform = T.Compose([T.ToTensor()])
+        self.threshold = 0.80
+        self.categories = weights.meta["categories"]
+        self.camera_hfov = math.radians(73.0)
+        self.latest_lidar_msg = None
+
+        # NEW: Now our memory tracks GLOBAL Map Coordinates, not Local Robot Coordinates!
+        self.human_last_global_x = None
+        self.human_last_global_y = None
+        self.last_seen_time = 0.0
+        
+        # NEW: Store the Robot's current position and rotation in the world
+        self.robot_global_x = 0.0
+        self.robot_global_y = 0.0
+        self.robot_global_yaw = 0.0
+        self.human_vx = 0.0
+        self.human_vy = 0.0
+        self.max_jump_distance = 0.32 
+        self.memory_timeout = 3.5     
+
+        self.lidar_cb_group = MutuallyExclusiveCallbackGroup()
+        self.camera_cb_group = MutuallyExclusiveCallbackGroup()
+        self.odom_cb_group = MutuallyExclusiveCallbackGroup()
+
+        self.active_properties = all_human_properties
+        self.active_trackers = None
+        self.frame_counter = 0
+        self.get_logger().info('Object Permanence Tracker with Global Odometry is Operational!')
+        
+        # Subscriptions
+        self.lidar_sub = self.create_subscription(
+            PoseArray, '/detected_objects', self.lidar_callback, 10, callback_group=self.lidar_cb_group)
+        self.camera_sub = self.create_subscription(
+            CompressedImage, '/oak/rgb/image_raw/compressed', self.camera_callback, 1, callback_group=self.camera_cb_group)
+        self.odom_sub = self.create_subscription(
+            Odometry, '/odometry/filtered', self.odom_callback, 10, callback_group=self.odom_cb_group)
+        # NEW: Debug Publisher to broadcast where the robot thinks the human is
+        self.debug_pub = self.create_publisher(Marker, '/debug/human_tracked_position', 10)
+
+    # NEW: Helper function to convert Robot Quaternion data into simple Yaw (rotation in radians)
+    def euler_from_quaternion(self, x, y, z, w):
+        t3 = +2.0 * (w * z + x * y)
+        t4 = +1.0 - 2.0 * (y * y + z * z)
+        return math.atan2(t3, t4)
+
+    # NEW: Keep track of exactly where the robot is in the world at all times
+    def odom_callback(self, msg):
+        with self.lock:
+            self.robot_global_x = msg.pose.pose.position.x
+            self.robot_global_y = msg.pose.pose.position.y
+            q = msg.pose.pose.orientation
+            self.robot_global_yaw = self.euler_from_quaternion(q.x, q.y, q.z, q.w)
+
+    def camera_callback(self, msg):
+        np_arr = np.frombuffer(msg.data, np.uint8)
+        img_bgr = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+        if img_bgr is None: return
+
+        img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+        img_tensor = self.transform(PILImage.fromarray(img_rgb))
+        img_w = img_bgr.shape[1]
+
+        with torch.no_grad():
+            prediction = self.model([img_tensor])
+
+        for box, score, label in zip(prediction[0]['boxes'], prediction[0]['scores'], prediction[0]['labels']):
+            if score > self.threshold:
+                if self.categories[label.item()] == 'person':
+                    x1, y1, x2, y2 = box.numpy().astype(int)
+                    box_center_x = (x1 + x2) / 2.0
+                    normalized_x = (box_center_x / img_w) - 0.5
+                    camera_angle = -normalized_x * self.camera_hfov
+                    self.set_ground_truth_from_camera(camera_angle)
+
+    def set_ground_truth_from_camera(self, camera_angle):
+        with self.lock:
+            if self.latest_lidar_msg is None: return
+            poses = self.latest_lidar_msg.poses
+            rob_x = self.robot_global_x
+            rob_y = self.robot_global_y
+            rob_yaw = self.robot_global_yaw
+            
+            # NEW: Pull the current human memory so the camera can check its math
+            hg_x = self.human_last_global_x
+            hg_y = self.human_last_global_y
+
+        best_match = None
+        min_physical_distance = 999.0 
+
+        for pose in poses:
+            x_local = -pose.position.x
+            y_local = -pose.position.y
+            lidar_angle = math.atan2(y_local, x_local)
+
+            angle_diff = abs(camera_angle - lidar_angle)
+            if angle_diff < math.radians(15.0):
+                distance_to_clump = math.hypot(x_local, y_local)
+                if distance_to_clump < min_physical_distance:
+                    min_physical_distance = distance_to_clump
+                    
+                    global_x = rob_x + (x_local * math.cos(rob_yaw)) - (y_local * math.sin(rob_yaw))
+                    global_y = rob_y + (x_local * math.sin(rob_yaw)) + (y_local * math.cos(rob_yaw))
+                    best_match = (global_x, global_y)
+                
+        if best_match is not None:
+            # --- NEW: DIAGNOSTIC AND JUMP LIMIT FOR THE CAMERA ---
+            if hg_x is not None and hg_y is not None:
+                jump_dist = math.hypot(best_match[0] - hg_x, best_match[1] - hg_y)
+                
+                # If the camera tries to teleport the human further than your limit, block it!
+                if jump_dist > self.max_jump_distance:
+                    self.get_logger().warn(f"CAMERA FALSE POSITIVE BLOCKED: Tried to jump {jump_dist:.2f}m")
+                    return # Exit the function immediately without updating memory
+            
+            # If it passes the test (or if we have no current memory), update the coordinates
+            with self.lock:
+                self.human_last_global_x, self.human_last_global_y = best_match
+                self.last_seen_time = self.get_clock().now().nanoseconds / 1e9
+            # -----------------------------------------------------
+
+    def lidar_callback(self, msg):
+        with self.lock:
+            self.latest_lidar_msg = msg
+            hg_x, hg_y = self.human_last_global_x, self.human_last_global_y
+            rob_x, rob_y, rob_yaw = self.robot_global_x, self.robot_global_y, self.robot_global_yaw
+            last_time = self.last_seen_time
+
+        if hg_x is None: return
+        current_time = self.get_clock().now().nanoseconds / 1e9
+        dt = current_time - last_time # Calculate time passed since last scan
+
+        # NEW: Predict where the human is based on their current walking speed
+        if dt > 0 and dt < 1.0:
+            pred_x = hg_x + (self.human_vx * dt)
+            pred_y = hg_y + (self.human_vy * dt)
+        else:
+            # If it's been too long, reset momentum
+            pred_x = hg_x
+            pred_y = hg_y
+            self.human_vx = 0.0
+            self.human_vy = 0.0
+        if current_time - last_time > self.memory_timeout:
+            self.get_logger().warn("Track lost! Human out of range. Resetting graph.")
+            self.frame_counter = 0
+            with self.lock:
+                self.human_last_global_x = None
+                self.human_last_global_y = None
+                self.active_trackers = None
+            return
+
+        self.frame_counter += 1
+        closest_distance = 999.0
+        best_global_x = None
+        best_global_y = None
+        best_local_x = None
+        best_local_y = None
+        
+        for pose in msg.poses:
+            x_local = -pose.position.x
+            y_local = -pose.position.y
+
+            # NEW: Convert EVERY Lidar clump into a Global coordinate before measuring distance
+            clump_global_x = rob_x + (x_local * math.cos(rob_yaw)) - (y_local * math.sin(rob_yaw))
+            clump_global_y = rob_y + (x_local * math.sin(rob_yaw)) + (y_local * math.cos(rob_yaw))
+
+            # NEW: The jump distance is now based purely on Global map coordinates, immune to robot movement!
+            jump_dist = math.hypot(clump_global_x - pred_x, clump_global_y - pred_y)
+            
+            if jump_dist < closest_distance:
+                closest_distance = jump_dist
+                best_global_x = clump_global_x
+                best_global_y = clump_global_y
+                
+                # We still save the local coordinates because the DFA rulebook (FRONT/LEFT/RIGHT) needs Ego-centric values!
+                best_local_x = x_local
+                best_local_y = y_local
+
+        if closest_distance <= self.max_jump_distance and best_global_x is not None:
+            with self.lock:
+                # NEW: Calculate how fast the human is moving to update their momentum
+                if dt > 0:
+                    inst_vx = (best_global_x - hg_x) / dt
+                    inst_vy = (best_global_y - hg_y) / dt
+                    
+                    # Smooth the velocity (50% old, 50% new) so it doesn't get jerky
+                    self.human_vx = (0.5 * inst_vx) + (0.5 * self.human_vx)
+                    self.human_vy = (0.5 * inst_vy) + (0.5 * self.human_vy)
+
+                # Update memory with the new Global position
+                self.human_last_global_x = best_global_x
+                self.human_last_global_y = best_global_y
+                self.last_seen_time = current_time
+            
+            #debug publisher:
+            debug_msg = Marker()
+            
+            # 1. Header info
+            debug_msg.header.stamp = self.get_clock().now().to_msg()
+            debug_msg.header.frame_id = 'odom' 
+            
+            # 2. Marker Configuration
+            debug_msg.ns = "human_tracker"
+            debug_msg.id = 0
+            debug_msg.type = Marker.CYLINDER # Draw a cylinder
+            debug_msg.action = Marker.ADD
+            
+            # 3. Position (Using your global math)
+            debug_msg.pose.position.x = best_global_x
+            debug_msg.pose.position.y = best_global_y
+            debug_msg.pose.position.z = 0.5 # Lift it half a meter off the floor
+            
+            # 4. Size (Human sized: 0.4m wide, 1.0m tall)
+            debug_msg.scale.x = 0.4
+            debug_msg.scale.y = 0.4
+            debug_msg.scale.z = 1.0
+            
+            # 5. Color (Bright Green, fully opaque)
+            debug_msg.color.r = 0.0
+            debug_msg.color.g = 1.0
+            debug_msg.color.b = 0.0
+            debug_msg.color.a = 1.0 
+            
+            self.debug_pub.publish(debug_msg)
+            
+            # The NetworkX graph still gets the local coordinates, so FRONT/LEFT/RIGHT is always relative to the camera
+            current_sg = self.build_networkx_graph(best_local_x, best_local_y)
+            
+            if self.active_trackers is None:
+                self.active_trackers = []
+                for prop in self.active_properties:
+                    self.active_trackers.extend(prop.make_concrete(current_sg))
+            
+            if self.active_trackers is not None:
+                for tracker in self.active_trackers:
+                    tracker.step(current_sg)
+                    if tracker.is_trap() and not tracker.is_accepting():
+                        self.get_logger().error("Trap State: Maneuver Violated!")
+                    elif tracker.is_trap():
+                        self.get_logger().info("Robot Successfully Passed on Left!  !]")
+                    else:
+                        self.get_logger().info("Accepting State: Human is at ("+ str(round(self.human_last_global_x*100)/100)+", "+str(round(self.human_last_global_y*100)/100)+")")
+    
+    def build_networkx_graph(self, human_x, human_y):
+        sg = nx.DiGraph()
+        ego_node = SceneNode(node_id=0, name="ego", base_class="vehicle")
+        human_node = SceneNode(node_id=1, name="person_1", base_class="person")
+        sg.add_node(ego_node)
+        sg.add_node(human_node)
+        
+        angle_rad = math.atan2(human_y, human_x)
+        angle_deg = math.degrees(angle_rad)
+
+        if -45.0 <= angle_deg <= 45.0: direction = "FRONT"
+        elif 45.0 < angle_deg <= 135.0: direction = "LEFT"
+        elif -135.0 <= angle_deg < -45.0: direction = "RIGHT"
+        else: direction = "BACK"
+            
+        sg.add_edge(ego_node, human_node, label=direction, type=direction, relation=direction)
+        sg.graph['frame'] = self.frame_counter
+        sg.graph['cache'] = {}
+        return sg
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = AsyncTrackingNode()
+    executor = MultiThreadedExecutor()
+    executor.add_node(node)
+    try:
+        executor.spin()
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        if rclpy.ok(): rclpy.shutdown()
+
+if __name__ == '__main__':
+    main()