Real-Time Object Detection Using a Sparse 4-Layer LIDAR

M.P. Muresan, S. Nedevschi, I. Giosan

Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 317-322.

The robust detection of obstacles, on a given road path by vehicles equipped with range measurement devices represents a requirement for many research fields including autonomous driving and advanced driving assistance systems. One particular sensor system used for measurement tasks, due to its known accuracy, is the LIDAR (Light Detection and Ranging). The commercial price and computational intensiveness of such systems generally increase with the number of scanning layers. For this reason, in this paper, a novel six step based obstacle detection approach using a 4-layer LIDAR is presented. In the proposed pipeline we tackle the problem of data correction and temporal point cloud fusion and we present an original method for detecting obstacles using a combination between a polar histogram and an elevation grid. The results have been validated by using objects provided from other range measurement sensors.


An approach for segmenting 3D LiDAR data using Multi-Volume grid structures

S.E.C. Goga, S. Nedevschi

Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 309-315.

This paper proposes a novel approach for segmenting and space partitioning data ofsparse 3D LiDAR point clouds for autonomous driving tasks in urban environments. Our main focus is building a compact data representation which provides enough information for an accurate segmentation algorithm. We propose the use of an extension of elevation maps for automotive driving perception tasks which is capable of dealing with both protruding and hanging objects found in urban scenes like bridges, hanging road barrier, traffic tunnels, tree branches over road surface, and so on. For this we use a MultiVolume grid representation of the environment. We apply a fast primary classifier in order to label the surface volumes as being part of the ground segment or of an object segment. Segmentation is performed on the object labeled data which is previously connected in a spatial graph structure using a height overlapping criterion. A comparison between the proposed method and the popular connected-components based segmentation method applied on an Elevation Map is performed in the end.


Online Cross-Calibration of Camera and LIDAR

B.C.Z. Blaga, S. Nedevschi

Proceedings of 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 7-9, 2017, pp. 295-301.

In an autonomous driving system, drift can affect the sensor’s position, introducing errors in the extrinsic calibration. For this reason, we have developed a method which continuously monitors two sensors, camera, and LIDAR with 16 beams, and adjusts the value of their cross-calibration. Our algorithm, starting from correct values of the extrinsic crosscalibration parameters, can detect small sensor drift during vehicle driving, by overlapping the edges from the LIDAR over the edges from the image. The novelty of our method is that in order to obtain edges, we create a range image and filter the data from the 3D point cloud, and we use distance transform on 2D images to find edges. Another improvement we bring is applying motion correction on laser scanner data to remove distortions that appear during vehicle motion. An optimization problem on the 6 calibration parameters is defined, from which we are able to obtain the best value of the cross-calibration, and readjust it automatically. Our system performs successfully in real time, in a wide variety of scenarios, and is not affected by the speed of the car.


Deliverable 8.3

Integration and test tools and processes

This deliverable describes the integration tools and the processes established by the consortium. The choice of the tools and the processes is based on best practices from previous collaborative robotic projects.


Deliverable 7.2

First development and integration cycle of decision making and navigation

This deliverable provides insights of the current software architecture representing the decision-making and navigation framework assigned to WP7 of the UP-Drive project which is eager to create a car capable of self-driving in urban traffic scenarios up to 30 km/h.


Deliverable 6.2

First development and integration cycle of scene understanding

This deliverable contributes to the Up-Drive project´s endeavor to create a car capable of self-driving in an unconstrained urban environment with speeds up to 30 km/h. D6.2 covers the WP6 (Scene/scenario understanding) related development and the first integration cycle of its implementation into the Up-Drive-wide system.


Deliverable 5.2

First development and integration cycle of lifelong mapping

This deliverable describes the lifelong mapping framework after the first development & integration cycle. All components, notably the metric and semantic map, the metric online localization, the semantic data aggregation and the map summarization are functional and integrated on the vehicles, fulfill their basic purposes and interact with each other in a limited fashion. All components deliver first evaluation results.


Deliverable 4.3

Initial version of higher-level perception functions

The deliverable provides an initial design and implementation of the higher level perception functions referring to road surface and obstacle perception, parking spot detection, road users classification, tracking and signaling perception.