Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots

Visual Odometry (VO) is increasingly a useful tool for robotic navigation in a variety of applications, including weed removal for agricultural robotics. The methods of evaluating VO are often computationally expensive and can cause the VO measurements to be significantly delayed with respect to a compass, wheel odometry, and GPS measurements. In this paper we present a Bayesian formulation of fusing delayed displacement measurements. We implement solutions to this problem based on the unscented Kalman filter (UKF), leading to what we term an unscented multi-point smoother. The proposed methods are tested in simulations of an agricultural robot. The simulations show improvements in the localization RMS error when including the VO measurements with a variety of latencies

Climate Change and Weeds of Cropping Systems

The impacts of weeds in cropping systems are diverse and costly. Direct expenditure on control and biosecurity measures costs society billions each year. Even with such heavy investment in prevention and control, weeds continue to reduce the quality and quantity of agricultural produce and represent a significant threat to global food production. The challenge of managing weeds in cropping systems is rendered increasingly complex given the diverse and unpredictable impacts of climate change on both weeds and crops. Atmospheric CO2, temperature and precipitation are key drivers of plant growth, and weeds, like all other plant species, will need to respond to climate change in order to survive. Weed species are by their very nature survivors, able to relocate, acclimate or adapt to changing environmental conditions, with genetic diversity that could confer a natural competitive advantage over crop species. Conversely, modern crops are the result of extensive and highly sophisticated breeding to improve their genetic potential to survive in challenging conditions, including herbicide application, limited soil moisture and high temperatures. Moreover, agricultural weeds evolve in highly managed environments, and management intervention through crop selection, crop planting strategies and weed control measures may exert stronger selection pressures on weed species relative to climate change. It is, however, reasonable to assert that evolution driven by management pressures could occur simultaneously to climate-driven adaptation. For this reason, even given the rapid advancement of increasingly sophisticated weed control technology, weed management now and in the future should be guided a sound understanding of evolutionary biology