Grid-Based Object Tracking
Zusammenfassung
Mobile robots require an accurate environment perception to plan intelligent maneuvers and avoid collisions. This thesis presents a novel multi sensor environment estimation strategy that fully combines tracking moving objects and mapping the static environment. The basic idea is to fuse and accumulate measurement data by a dynamic occupancy grid model, whereas moving objects are extracted subsequently based on that generic low-level grid representation. Overall, this work results in a robust and consistent estimation of arbitrary objects and obstacles, which is demonstrated in the context of autonomous driving in complex unstructured environments.
Contents
Notations VIII
Abstract XI
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Challenges of Multi-Sensor Environment Perception . . . . . . . . . . . . . 2
1.3 Main Contribution and Outline of This Work . . . . . . . . . . . . . . . . 8
2 Measurement Grid Representation and Fusion 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.2...
Schlagworte
Autonomes Fahren Objekterkennung Objektverfolgung Occupancy Grid Mapping Sensordatenassoziation Sensordatenfusion Umfelderfassung Umgebungswahrnehmung Zustandsschätzung Autonomous Vehicles Data Association Environment Perception Moving Object Detection Object State Estimation Object Tracking Sensor Data Fusion- 1–12 1 Introduction 1–12
- 127–164 6 Evaluation 127–164
- 165–168 7 Conclusion 165–168
- 169–169 Own Publications 169–169
- 170–186 Bibliography 170–186
