We design, build and run data-driven systems at petabyte scale.
We analyse your data and generate insights for your team.
We handle your infrastructure so you can focus on you business.
Stop converting and splitting ROS bags! Now you can load ROS topics natively in Spark for data preperation, exploration, and feature extraction with 80+ operators. Process your sensor data 100x faster using Java, Scala, Python, R, and SQL.
Load sensor data out of ROS bags natively with TensorFlow and Keras for object detection, data fusion, trajectory prediction, and motion control.
# Load ROSbag data from HDFS in Spark df = spark.read.rosbag("hdfs://test-drive.bag") # Search in IMU data using SQL imu = spark.sql("SELECT linear_acceleration FROM rosbagFile WHERE x > 1 AND z >= 10") imu.plot()
# Load ROS bag data from HDFS in TensorFlow files = tf.data.Dataset .list_files("hdfs://dataset/bags-*.tfrecord") # Train model on images (train_images, train_labels), (test_images, test_labels) = files.load_data() ... model.fit(train_images, train_labels, epochs=5) test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc) Test accuracy: 0.8778
Parallel processing with fast serialization between nodes and clusters to support massive sensor data out of ROS bags. Distributed machine learning on big data delivers speeds up to 100x faster with fine-grain parallelism.
Use Lidar, GPS, IMU raw data to perform map generation and point cloud alignment. You can use SLAM/pose estimation to derive the location. Add labels and semantic information to the grid map. Speed up iterative closest point operations and point cloud alignment.
The dashboard automatically updates visualizations and creates data plots that are most relevant from a statistical perspective based on sensor data statistics.
# Load ROSbag data from S3 in TensorFlow files = tf.data.Dataset .list_files("s3://bags-*.tfrecord") # Train model on images ...
# Load ROSbag data from HDFS in TensorFlow files = tf.data.Dataset .list_files("hdfs://bags-*.tfrecord") # Train model on images ...
# Load ROSbag data Local FS in TensorFlow files = tf.data.Dataset .list_files("bags-*.tfrecord") # Train model on images ...
Connect your data sources and get instant quality checks to easily rate sensor data and their quality. Now you can process data formats natively like ROS bag, MDF4, HDF5, and PCAP without converting and splitting. Save compute time and storage costs.