Initial commit

2026-03-04 00:08:42 +00:00
commit 4d88dc0584
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+//! LiDAR point cloud processing
+
+use crate::{VisionResult, VisionError};
+use crate::detection::three_d::{PointCloud, Detection3DResult, BoundingBox3D};
+use crate::autonomous::{LidarData, AutonomousDetector, SensorType};
+use crate::tensor_utils::TensorExt;
+use rtx_tensor::{Tensor, Device, DType};
+
+/// LiDAR-based object detector
+pub struct LidarDetector {
+    point_net: PointNetBackbone,
+    detection_head: LidarDetectionHead,
+}
+
+impl LidarDetector {
+    pub fn new() -> VisionResult<Self> {
+        let point_net = PointNetBackbone::new()?;
+        let detection_head = LidarDetectionHead::new(1024, 10)?; // 10 classes
+        
+        Ok(Self {
+            point_net,
+            detection_head,
+        })
+    }
+    
+    /// Process point cloud for object detection
+    pub fn process_point_cloud(&mut self, point_cloud: &PointCloud) -> VisionResult<Vec<BoundingBox3D>> {
+        // Extract features
+        let features = self.point_net.forward(&point_cloud.points)?;
+        
+        // Detect objects
+        let detections = self.detection_head.forward(&features)?;
+        
+        Ok(detections)
+    }
+}
+
+impl AutonomousDetector for LidarDetector {
+    fn detect_lidar(&mut self, lidar_data: &LidarData) -> VisionResult<Detection3DResult> {
+        let start_time = std::time::Instant::now();
+        
+        let detections = self.process_point_cloud(&lidar_data.point_cloud)?;
+        
+        let processing_time = start_time.elapsed().as_millis() as f32;
+        
+        Ok(Detection3DResult {
+            boxes_3d: detections,
+            processing_time_ms: processing_time,
+            model_name: "LiDAR Detector".to_string(),
+        })
+    }
+    
+    fn supported_sensors(&self) -> Vec<SensorType> {
+        vec![SensorType::LiDAR]
+    }
+}
+
+/// PointNet backbone for LiDAR processing
+struct PointNetBackbone {
+    conv_layers: Vec<Tensor>,
+}
+
+impl PointNetBackbone {
+    fn new() -> VisionResult<Self> {
+        let conv_layers = vec![
+            Tensor::randn(&[64, 3, 1], &Device::default())?,
+            Tensor::randn(&[128, 64, 1], &Device::default())?,
+            Tensor::randn(&[1024, 128, 1], &Device::default())?,
+        ];
+        
+        Ok(Self { conv_layers })
+    }
+    
+    fn forward(&self, points: &Tensor) -> VisionResult<Tensor> {
+        let mut x = points.transpose(-2, -1)?; // (B, 3, N)
+        
+        for conv in &self.conv_layers {
+            x = x.conv1d(conv, 0, 1, 1)?.relu()?;
+        }
+        
+        // Global max pooling
+        let global_features = x.max_dim(2, true)?;
+        Ok(global_features.0)
+    }
+}
+
+/// LiDAR detection head
+struct LidarDetectionHead {
+    fc_layers: Vec<Tensor>,
+    num_classes: usize,
+}
+
+impl LidarDetectionHead {
+    fn new(in_features: usize, num_classes: usize) -> VisionResult<Self> {
+        let fc_layers = vec![
+            Tensor::randn(&[512, in_features], &Device::default())?,
+            Tensor::randn(&[256, 512], &Device::default())?,
+            Tensor::randn(&[(num_classes + 7) * 100, 256], DType::F32, &Device::default())?, // 7 for box params, 100 proposals
+        ];
+        
+        Ok(Self { fc_layers, num_classes })
+    }
+    
+    fn forward(&self, features: &Tensor) -> VisionResult<Vec<BoundingBox3D>> {
+        let mut x = features.clone();
+        
+        for fc in &self.fc_layers[..2] {
+            x = x.matmul(&fc.transpose(-2, -1)?)?.relu()?;
+        }
+        
+        let predictions = x.matmul(&self.fc_layers[2].transpose(-2, -1)?)?;
+        
+        // Decode predictions
+        self.decode_predictions(&predictions)
+    }
+    
+    fn decode_predictions(&self, predictions: &Tensor) -> VisionResult<Vec<BoundingBox3D>> {
+        let pred_data = predictions.to_vec()?;
+        let mut boxes = Vec::new();
+        
+        let stride = self.num_classes + 7;
+        for i in (0..pred_data.len()).step_by(stride) {
+            if i + 6 < pred_data.len() {
+                let center = [pred_data[i], pred_data[i + 1], pred_data[i + 2]];
+                let size = [pred_data[i + 3], pred_data[i + 4], pred_data[i + 5]];
+                let rotation = [pred_data[i + 6], 0.0, 0.0];
+                
+                // Find best class
+                let mut best_class = 0;
+                let mut best_confidence = 0.0;
+                
+                for class_id in 0..self.num_classes {
+                    let conf_idx = i + 7 + class_id;
+                    if conf_idx < pred_data.len() {
+                        let confidence = pred_data[conf_idx].sigmoid();
+                        if confidence > best_confidence {
+                            best_confidence = confidence;
+                            best_class = class_id;
+                        }
+                    }
+                }
+                
+                if best_confidence > 0.3 {
+                    boxes.push(BoundingBox3D::new(center, size, rotation, best_confidence, best_class));
+                }
+            }
+        }
+        
+        Ok(boxes)
+    }
+}
+
+/// Point cloud preprocessing utilities
+pub struct LidarPreprocessor;
+
+impl LidarPreprocessor {
+    /// Remove ground points using RANSAC plane fitting
+    pub fn remove_ground_plane(point_cloud: &PointCloud) -> VisionResult<PointCloud> {
+        // Simplified ground removal - in practice use proper RANSAC
+        Ok(point_cloud.clone())
+    }
+    
+    /// Voxel downsampling to reduce point density
+    pub fn voxel_downsample(point_cloud: &PointCloud, voxel_size: f32) -> VisionResult<PointCloud> {
+        point_cloud.voxelize(voxel_size).map(|voxels| {
+            PointCloud::new(voxels.voxel_coords)
+        })
+    }
+    
+    /// Statistical outlier removal
+    pub fn remove_outliers(point_cloud: &PointCloud, k_neighbors: usize, std_multiplier: f32) -> VisionResult<PointCloud> {
+        // Simplified outlier removal
+        Ok(point_cloud.clone())
+    }
+}