This paper describes the software and hardware platform used by the Innopolis University team to conduct investigations in the field of self-driving vehicles. The paper also describes basic technical solutions and the grounds for decisions for each key subsystems: localization, route planning, control, object perception and prediction of their behavior. The developed software and hardware platform was implemented for KAMAZ truck and KIA electric car navigation in various navigation scenarios. The article presents the results of testing of the platform for both of those vehicles.

This paper presents a method for classification and localization of road signs in a 3D space, which is done with a help of neural network and point cloud obtained from a laser range finder (LIDAR). In addition, to accomplish this task and train the neural network (which is based on Faster-RCNN architecture) a dataset was collected. The trained convolutional network is used as a part of ROS node which fuses the obtained classification, data from the camera and lidar measurements. The output of the system is a set of images with bounding boxes and point clouds, corresponding to real signs on the road. The introduced method was tested and performed well on a dataset acquired from a self-driving car during different road conditions.

The efficiency and execution time of a road scene evaluation subsystem directly influences a self-driving car's control effectiveness. This article presents a novel approach to fuse data from various sensors (camera, LIDAR, radar, IMU) for pedestrian and vehicle tracking. Our approach, thanks to modern methods of image processing and power of GPU for LIDAR processing, achieves 25Hz update frequency for tracking. The system has been tested on the DiDi-Udacity Self-Driving Challenge datasets.

Car racing control is often assumed to take place in isolation, although the greatest strategic challenges arise in the presence of other cars. Our solution uses a combined nonlinear system model which includes the four-wheeled vehicle dynamics model and point-mass model of its opponent car. This proposed combined model allows the optimal control scheme to automatically find an overtaking strategy. The FORCES code generation tool to handle the consequent computational complexity of the Nonlinear Model Predictive Controller (NMPC). We demonstrate the practicality of the suggested method by performing real-time control in a racing simulation software.

This paper presents a comparison of four most recent ROS-based monocular SLAM-related methods: ORB-SLAM, REMODE, LSD-SLAM, and DPPTAM, and analyzes their feasibility for a mobile robot application in indoor environment. We tested these methods using video data that was recorded from a conventional wide-angle full HD webcam with a rolling shutter. The camera was mounted on a human-operated prototype of an unmanned ground vehicle, which followed a closed-loop trajectory. Both feature-based methods (ORB-SLAM, REMODE) and direct SLAMrelated algorithms (LSD-SLAM, DPPTAM) demonstrated reasonably good results in detection of volumetric objects, corners, obstacles and other local features. However, we met difficulties with recovering typical for offices homogeneously colored walls, since all of these methods created empty spaces in a reconstructed sparse 3D scene. This may cause collisions of an autonomously guided robot with unfeatured walls and thus limits applicability of maps, which are obtained by the considered monocular SLAM-related methods for indoor robot navigation.