Abstract

Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

Highlights

  • In recent years, autonomous robots and systems have influenced the automation of various agricultural tasks

  • We evaluate the process evaluation on the mapped data with a spatial resolution of 10 cm per cell

  • The publicly available FieldSAFE dataset (Kragh et al, 2017) for multi-modal obstacle detection in agricultural fields was used for the evaluation

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Summary

Introduction

Autonomous robots and systems have influenced the automation of various agricultural tasks. Conventional scenarios still have the human operator in a centralized position of the farming process, supported by various noncentralized controls units. Due to the global trend in automation, the operator will become an observer in upcoming farming scenarios and to a greater extent manage than operate the process. One key aspect of reaching this goal is to ensure safe operation of driverless systems by perceiving. Obstacle Detection and Process Evaluation the environment from which potential obstacles are detected and avoided. No sensor can single-handedly guarantee this safety in diverse agricultural environments, and, a heterogeneous and redundant set of perception sensors and algorithms are needed for this purpose

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