A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) has the potential for advancing autonomous object geolocation performance. Much research has focused on algorithmic improvements on individual components, such as navigation, motion planning, and perception. In this paper, we present a UGV-UAV object detection and geolocation system, which performs perception, navigation, and planning autonomously in real scale in unstructured environment. We designed novel sensor pods equipped with multispectral (visible, near-infrared, thermal), high resolution (181.6 Mega Pixels), stereo (nearinfrared pair), wide field of view (192 degree HFOV) array. We developed a novel on-board software-hardware architecture to process the high volume sensor data in real-time, and we built a custom AI subsystem composed of detection, tracking, navigation, and planning for autonomous objects geolocation in real-time.This research is the first real scale demonstration of such high speed data processing capability. Our novel modular sensor pod can boost relevant computer vision and machine learning research. Our novel hardware-software architecture is a solid foundation for system-level and component-level research. Our system is validated through data-driven offline tests as well as a series of field tests in unstructured environments. We present quantitative results as well as discussions on key robotic system level challenges which manifest when we build and test the system. This system is the first step toward a UGV-UAV cooperative reconnaissance system in the future.
Structured light techniques have been used in a lot of applications. As a two-dimensional optical measurement method, structured light sensors are faster than one-dimensional point triangulation sensor while easier to calibrate and move than full-field three-dimensional sensors. The accuracy of structured light sensors mainly depends on the accuracy of both calibration and beam center extraction. In some applications with complicated surface shapes, the extracted center may not be the actual "true" center, which results in image bias. This paper presents a method to compensate the image bias and improve the measurement accuracy of structured light sensors. The basic concepts of image bias correction are given and some initial results are provided.
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