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WHAT WE DO
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Data Engineering

We design, build and run data-driven systems at petabyte scale.

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Data Science

We analyse your data and generate insights for your team.

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DevOps

We handle your infrastructure so you can focus on you business.

ANALYTICS TOOLCHAIN

Large-scale sensor data processing

Analyze ROS bags with Apache Spark

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.

Train models with TensorFlow on ROS bags

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   
                                    

Train, test, and validate your models

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.

Generate HD Maps in the cloud

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.

Metrics and Visualizations to stay updated

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

  ...    
                                                  

Load data directly from S3, HDFS, ...

Azure, Google Cloud, or any data source

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.

Ready to get started? Get in touch!

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