The use of different modalities improves the perception of the environment in situations where the conventional sensors fail (camera and LiDAR). The inclusion of these modalities, such as sonar or radar, is however difficult as existing methods for the conventional sensors usually do not generalise well on these different environment representations. We experiment with a modality prediction method to keep using the existing methodologies and allow to separate the sensing system from the navigation stack of an autonomous agent. In previous work, we used a convolutional stacked autoencoder to predict LiDAR point cloud data using the data from our 3D in-air acoustic ultrasonic sensor (eRTIS). In this paper, we investigate the usability of the predicted data in off-the-shelf algorithms to safely navigate environments where visual modalities become unreliable and less accurate.

Sensors using ultrasonic sound have proven to provide accurate 3D perception in difficult environments where other modalities fail. Several industrial sectors need accurate and reliable sensing in these harsh conditions. The conventional LiDAR/camera approach in many state-of-the-art autonomous navigation methods is limited to environments with optimal sensing conditions for visual modalities. The use of other sensing modalities can thus improve reliability and usability and increase the application potential of autonomous agents. Ultrasonic measurements provide, compared to LiDAR, a much sparser representation of the environment, making a direct replacement of the LiDAR sensor difficult. In this work, we propose a method to predict LiDAR point cloud data from an in-air acoustic sonar sensor using a convolutional stacked autoencoder. This provides a robotic system with high-resolution measurements and allows for easier integration into existing systems to safely navigate environments where visual modalities become unreliable and less accurate. A video of our predictions is available at https://youtu.be/jlx1S-tslmo.

Current Software Defined Networking (SDN) techniques allow improving network control and flexibility. However its use in IoT is not trivial because IoT networks are unreliable and highly resource-constrained. Among some of the existing solutions proposed in the literature, Whisper enables SDN-like control over the packet forwarding and cell allocation of IoT devices by injecting in the network artificial, but still standard compliant messages that alter the default protocol behavior. Since Whisper uses carefully computed routing and scheduling messages that are compatible with the distributed protocols run in the network, it reduces the overhead in the network and operates without modifying the IoT devices' firmware. However, as other SDN-on-IoT technologies, Whisper is currently limited to the IoT network scope and remains as yet another independent network management silo. In this paper we propose a new higherlevel architecture that allows to fully integrate the IoT SDN network management into a network operating system, such as ONOS, by using Whisper in order to provide an integral endto-end softwarization. We also describe the interaction between the Whisper platform and the orchestrator and test our solution with real 6TiSCH-compatible hardware in the ONOS platform. Finally, we discuss the requirements and technical challenges to fully leverage Whisper to provide an efficient and programmable end-to-end control over an heterogeneous network domain.