Abstract

We present the current state of development of the sensor-equipped car MODISSA, with which Fraunhofer IOSB realizes a configurable experimental platform for hardware evaluation and software development in the context of mobile mapping and vehicle-related safety and protection. MODISSA is based on a van that has successively been equipped with a variety of optical sensors over the past few years, and contains hardware for complete raw data acquisition, georeferencing, real-time data analysis, and immediate visualization on in-car displays. We demonstrate the capabilities of MODISSA by giving a deeper insight into experiments with its specific configuration in the scope of three different applications. Other research groups can benefit from these experiences when setting up their own mobile sensor system, especially regarding the selection of hardware and software, the knowledge of possible sources of error, and the handling of the acquired sensor data.

© 2021 Optical Society of America

Effects of lidar and radar resolution on DNN-based vehicle detection

Itai Orr, Harel Damari, Meir Halachmi, Mark Raifel, Kfir Twizer, Moshik Cohen, and Zeev Zalevsky
J. Opt. Soc. Am. A 38(10) B29-B36 (2021)

Infrared object classification with a hybrid optical convolution neural network

Jianbo Chen, Jennifer Talley, and Kevin F. Kelly
Appl. Opt. 60(25) G224-G231 (2021)

Classification of airborne 3D point clouds regarding separation of vegetation in complex environments

Dimitri Bulatov, Dominik Stütz, Jorg Hacker, and Martin Weinmann
Appl. Opt. 60(22) F6-F20 (2021)

Detection of unmanned aerial vehicles using a visible camera system

Shuowen Hu, Geoffrey H. Goldman, and Christoph C. Borel-Donohue
Appl. Opt. 56(3) B214-B221 (2017)

Adaptive point cloud acquisition and upsampling for automotive lidar

Ljubomir Jovanov, Wei-Yu Lee, and Wilfried Philips
Appl. Opt. 62(17) F8-F13 (2023)