Interlacing Self Localization Moving Object Tracking And Mapping For 3d Range Sensors
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| Author | : Frank Moosmann |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 154 |
| Release | : 2014-05-13 |
| Genre | : Computers |
| ISBN | : 3866449771 |
This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects.
| Author | : Knorr, Moritz |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 166 |
| Release | : 2018-12-19 |
| Genre | : Calibration |
| ISBN | : 373150765X |
Multi-camera systems are being deployed in a variety of vehicles and mobile robots today. To eliminate the need for cost and labor intensive maintenance and calibration, continuous self-calibration is highly desirable. In this book we present such an approach for self-calibration of multi-Camera systems for vehicle surround sensing. In an extensive evaluation we assess our algorithm quantitatively using real-world data.
| Author | : Rabe, Johannes |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 196 |
| Release | : 2019-01-10 |
| Genre | : Augmented reality |
| ISBN | : 3731508540 |
This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations.
| Author | : Henning Lategahn |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 146 |
| Release | : 2014 |
| Genre | : Computers |
| ISBN | : 373150135X |
In this work we present a system to fully automatically create a highly accurate visual feature map from image data acquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving.
| Author | : Naumann, Maximilian |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 192 |
| Release | : 2021-02-25 |
| Genre | : Technology & Engineering |
| ISBN | : 3731510707 |
In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.
| Author | : Taş, Ömer Şahin |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 222 |
| Release | : 2023-10-23 |
| Genre | : |
| ISBN | : 3731512998 |
This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.
| Author | : Andreas Geiger |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 196 |
| Release | : 2014-07-29 |
| Genre | : Computers |
| ISBN | : 3731500817 |
This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences.
| Author | : Firl, Jonas |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 176 |
| Release | : 2015-01-07 |
| Genre | : Technology (General) |
| ISBN | : 3731502879 |
In this work an approach is presented to model and recognize traffic maneuvers in terms of interactions between different traffic participants on extra urban roads. Results of the recognition concept are presented and evaluated using different sensor setups and its benefit is outlined by an integration into a software framework in the field of Car-to-Car (C2C) communications. Furthermore, recognition results are used in this work to robustly predict vehicle's trajectories while driving dynamic.
| Author | : Tian, Wei |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 178 |
| Release | : 2019-05-21 |
| Genre | : Technology & Engineering |
| ISBN | : 3731509156 |
This work proposes novel approaches for object tracking in challenging scenarios like severe occlusion, deteriorated vision and long range multi-object reidenti?cation. All these solutions are only based on image sequence captured by a monocular camera and do not require additional sensors. Experiments on standard benchmarks demonstrate an improved state-of-the-art performance of these approaches. Since all the presented approaches are smartly designed, they can run at a real-time speed.
| Author | : Schoenbein, Miriam |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 156 |
| Release | : 2015-04-22 |
| Genre | : Technology (General) |
| ISBN | : 3731503573 |
Environment perception with cameras is an important requirement for many applications for autonomous vehicles and robots. This work presents a stereoscopic omnidirectional camera system for autonomous vehicles which resolves the problem of a limited field of view and provides a 360° panoramic view of the environment. We present a new projection model for these cameras and show that the camera setup overcomes major drawbacks of traditional perspective cameras in many applications.
