Vector search can detect anomalies in LIDAR data by comparing patterns in high-dimensional point clouds to identify deviations from normal scenarios. LIDAR data typically consists of 3D coordinates representing surfaces, objects, or environments. By converting these points into numerical vectors (embeddings) that capture spatial, geometric, or contextual features, vector search algorithms can efficiently measure similarities between different regions or frames of data. Anomalies—such as unexpected objects, structural defects, or sensor errors—appear as vectors that differ significantly from those in a reference dataset or predefined norms. This approach leverages the scalability of vector databases and similarity metrics like cosine distance to flag outliers in real-time or batch processing workflows.
For example, consider an autonomous vehicle using LIDAR to detect obstacles. During operation, the system might generate embeddings for segments of the point cloud, such as road surfaces, pedestrians, or vehicles. These embeddings could encode features like object shape, size, or distance from the sensor. A vector database could store embeddings from thousands of “normal” driving scenarios. When new LIDAR data is captured, the system searches the database for the closest matching vectors. If a segment’s vector has low similarity to all stored entries (e.g., an oddly shaped object in the middle of a highway), it’s flagged as a potential anomaly. Similarly, in infrastructure inspection, sudden changes in the vector representation of a bridge’s structure could indicate damage not present in historical scans.
Technically, this involves preprocessing LIDAR data into embeddings using methods like voxel grids (3D grids that group points) or neural networks trained to extract spatial features. Tools like FAISS or Milvus can index these vectors for fast similarity searches. Developers might use k-nearest neighbors (k-NN) algorithms to compare a query vector against the indexed dataset, with anomalies identified if the closest matches exceed a distance threshold. For instance, a cluster of LIDAR points representing a tree branch on a road might be flagged if its vector isn’t close to any “clear road” vectors in the index. This method balances accuracy and performance, making it practical for real-time systems where latency matters, such as robotics or safety-critical applications.