【行业分析】人工智能智能机器人在军事领域中的应用前景和发展趋势

    import cv2
    
    class LidarLocalization():
    def __init__(self):
        pass
    
    def detect_obstacle(self, scan_range):
        """
        Detect obstacles in a certain range from the laser scanner
    
        Args:
            scan_range: float - The maximum detection distance of the lidar
    
        Returns:
            List of obstacle positions in xy coordinates (meters). 
        """
    
        # Initialize variables for storing detected obstacles
        detected_objects = []
    
        # Get current pose estimate of the robot [x y theta]
        x = self.pose[0]   # x position (meters)
        y = self.pose[1]   # y position (meters)
        th = self.pose[2]  # heading angle (radians)
    
        # Loop over each scan point
        for i in range(-90, 90+1):
            # Compute sine and cosine of elevation angle of the current ray
            sin_th = math.sin((i * np.pi)/180)
            cos_th = math.cos((i * np.pi)/180)
    
            # Calculate cartesian coordinates of end points of the ray
            xp = x + (scan_range * cos_th)
            yp = y + (scan_range * sin_th)
    
            # Add detected objects to list if they are within max_distance of sensor
            obj = {
                'pos': np.array([xp,yp]),
                'label': None,
                'prob': None
            }
            detected_objects.append(obj)
    
        return detected_objects
    
    def get_xy_position(self, angle):
        """
        Convert polar coordinates of current lidar scan measurement into xy coordinates of object
    
        Args:
            angle: float - Angle of measurement relative to vehicle x axis (degrees)
            dist: float - Distance measured by lidar (meters)
    
        Returns:
            Position of object in xy coordinates (meters)
        """
    
        # Convert input angle and distance into radians and meters
        angle *= np.pi / 180
        dist *= 1000
    
        # Get x and y components of vector pointing towards object
        dx = dist * np.cos(angle)
        dy = dist * np.sin(angle)
    
        # Return result as numpy array with shape (2,) representing xy position in meters
        return np.array([dx,dy])
    
    def lidar_callback(self, data):
        """
        Callback function for receiving lidar data during localization process
    
        Args:
            data: ros message containing information about the laserscan measurements
                 ranges: Float32MultiArray - Array of distances to detected obstacles
                 angles: std_msgs::Float32MultiArray - Array of angles corresponding to ranges
                 timeStamp: rospy::Time - Timestamp of when scan was received
    
        Returns:
            None
        """
        # Extract relevant info from ROS message
        ranges = np.array(data.ranges)
        angles = np.array(data.angles)
    
        # Determine number of scans received so far
        num_scans += 1
    
        # If at least three complete scans have been collected, perform localization
        if num_scans >= 3:
            start_time = timer()
    
            # Step 1: Find all candidate laser rays that correspond to obstacles in scan range
            candidate_rays = []
            for i in range(len(ranges)):
                if not np.isinf(ranges[i]):
                    valid_ray = True
    
                    # Check if ray is too close to any edge or another obstacle
                    center_dist = self._compute_distance(center_xy, self.get_xy_position(angles[i]))
                    for j in range(len(detected_objects)):
                        other_dist = self._compute_distance(detected_objects[j]['pos'], self.get_xy_position(angles[i]))
    
                        if center_dist < other_dist:
                            valid_ray = False
                            break
    
                    if valid_ray:
                        candidate_rays.append({
                            'index': i,
                            'theta': angles[i],
                            'dist': ranges[i]
                        })
    
            # Step 2: Fit line model to rays with highest peak response value (best fit to non-background noise)
            valid_candidate_rays = sorted(candidate_rays, key=lambda k: k['dist'])[:min(num_samples, len(valid_rays))]
            X = [[math.cos(v['theta']), math.sin(v['theta'])] for v in valid_candidate_rays]
            Y = [v['dist'] for v in valid_candidate_rays]
            w = np.linalg.lstsq(X, Y)[0]
    
            # Step 3: Refine estimated pose using EKF filter
            self.process_ekf(w, valid_candidate_rays, t)
    
            # Print timing results
            print("Localizing took %fs"%(timer()-start_time))
    
    
            # Update visualization tools if necessary
            if visualize:
                self.update_visualization()
    
            # Reset counter for next set of scans
            num_scans = 0

    from keras.applications import VGG16
    from keras.preprocessing.image import load_img
    from keras.preprocessing.image import img_to_array
    from matplotlib import pyplot
    import os
    
    # Load pre-trained VGG16 model
    model = VGG16(weights='imagenet')
    
    def preprocess_input(x):
    """Preprocesses image input before passing it through network"""
    x /= 255.
    x -= 0.5
    x *= 2.
    return x
    
    def depth_perception(frame):
    """Performs depth estimation on provided frame using VGG16"""
    
    # Preprocess input frame for CNN
    width, height = frame.shape[:2]
    if width!= target_width or height!= target_height:
        im = cv2.resize(frame, dsize=(target_width, target_height), interpolation=cv2.INTER_AREA)
    else:
        im = frame
    
    x = img_to_array(im)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    
    # Make prediction on image using VGG16 model
    features = model.predict(x)
    
    return features
    
    def track_obstacle(features, tracked_objects=[], dt=None, min_iou=0.3, max_age=2):
    """Tracks obstacles given their feature representation"""
    
    # Define parameters used for filtering candidates for assignment
    cell_size = 10.    # Size of grid cells (meters)
    max_speed = 0.5     # Maximum speed allowed between frames (meters/sec)
    iou_threshold = 0.5 # Minimum intersection-over-union required for two tracks to match
    
    # Create empty dictionary to store tracked obstacles
    tracker = {}
    
    # Split incoming detections into left and right halves
    ftr_left, ftr_right = split_detections(features)
    
    # Iterate over detections and perform initial assignment or reassignment depending on whether previous assignments exist
    for det in ftr_left + ftr_right:
    
        found_match = False
        best_track = None
        max_iou = 0.0
    
        # Iterate over existing tracks to find potential matches
        for track in tracked_objects:
            age = abs(dt - track['last_seen']).total_seconds()
            if age > max_age:
                continue
    
            # Calculate IoU score between current detection and associated track
            overlap = calculate_overlap(det['box'], track['box'])
            if overlap > max_iou:
                best_track = track
                max_iou = overlap
    
        # Perform reassignment if overlapping with known track with sufficient IOU score
        if best_track is not None and max_iou > min_iou:
            assigned_tracks[t].append({'feature': det['feature'], 'box': det['box'],'score': max_iou})
            bboxes.append(det['box'])
            scores.append(max_iou)
            found_match = True
    
        # Otherwise, create new track with no prior association
        elif best_track is None or max_iou <= min_iou:
            new_track = {'feature': det['feature'], 'box': det['box'], 'first_seen': datetime.datetime.now(),
                         'last_seen': datetime.datetime.now()}
            tracked_objects.append(new_track)
            bboxes.append(det['box'])
            scores.append(1.0)
            num_tracked += 1
            found_match = True
    
    # Use hungarian algorithm to associate remaining unassigned detections with remaining tracks
    cost_matrix = np.zeros((num_detections, num_tracked))
    for i in range(cost_matrix.shape[0]):
        box_a = ftr_left[i]['box'] if i < num_detections // 2 else ftr_right[i - num_detections//2]['box']
        for j in range(cost_matrix.shape[1]):
            track = tracked_objects[j]
            box_b = track['box']
            overlap = calculate_overlap(box_a, box_b)
            cost_matrix[i][j] = 1.0 - overlap
    
    row_ind, col_ind = scipy.optimize.linear_sum_assignment(cost_matrix)
    
    # Assign remaining unassigned detections to remaining unassigned tracks
    for i in range(cost_matrix.shape[0]):
        if row_ind[i] == -1:
            det = ftr_left[row_ind[i]] if i < num_detections // 2 else ftr_right[i - num_detections//2]
            track = random.choice(unassigned_tracks)
            tracked_objects[track].update({'feature': det['feature'], 'box': det['box'],
                                            'first_seen': datetime.datetime.now(),
                                            'last_seen': datetime.datetime.now()})
            assigned_tracks[t].append({'feature': det['feature'], 'box': det['box'],'score': 1.0})
            bboxes.append(det['box'])
            scores.append(1.0)
            unassigned_tracks.remove(track)
            num_tracked += 1
    
    # Remove lost tracks after minimum observation period has elapsed
    for track in list(tracked_objects):
        age = abs(dt - track['last_seen']).total_seconds()
        if age > max_age:
            tracked_objects.remove(track)
            unassigned_tracks.add(idx)
    
    
    # Visualize resulting tracks
    visualizer = ObjectVisualizer(frame, color=(0, 255, 0))
    for idx, track in enumerate(tracked_objects):
        label = "Untracked" if idx in unassigned_tracks else str(len(assigned_tracks)-1-idx)
        visualizer.draw_bounding_box(track['box'][0], track['box'][1], track['box'][2]-track['box'][0],
                                      track['box'][3]-track['box'][1], name=label)
    frame = visualizer.render()
    
    return frame, assigned_tracks
    
    # Set up path to test images directory
    test_images_dir = './test_images/'
    
    # Set up lists to hold assigned detections and their corresponding labels
    labels = ['car', 'person', 'traffic light']
    assigned_tracks = [{k:[] for k in labels} for _ in range(len(os.listdir(test_images_dir)))]
    
    # Test performance on several test images
    for file in os.listdir(test_images_dir):
    
    # Read in frame
    frame = cv2.imread(os.path.join(test_images_dir, file))
    
    # Perform depth perception and assign detections to appropriate tracks
    features = depth_perception(frame)
    processed_frame, assigned_tracks[file[:-4]] = track_obstacle(features, [], dt=None, min_iou=0.5, max_age=1.)
    
    # Display output
    cv2.imshow('Processed Image', processed_frame)
    cv2.waitKey(0)