3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. This task can be carried out via view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information, improving the resulting 3D reconstruction. Nonetheless, in most real-world settings, existing environmental noise can significantly affect the performance of 3D reconstruction. To that end, this work advocates a novel geometric-based reconstruction quality function for VP, that accounts for the existing noise of the environment, without requiring its closed-form expression. With no analytic expression of the objective function, this work puts forth an adaptive Bayesian optimization algorithm for accurate 3D reconstruction in the presence of noise. Numerical tests on noisy agricultural environments showcase the merits of the proposed approach for 3D reconstruction with even a small number of available cameras.

The determination of a crop's growth-stage is critical information for precision agriculture. Estimates of the growth-stage are used to guide irrigation and the application of agrochemicals. Of particular importance is the use of fertilizers, however, growth-stage estimates may also suggest further investigation of potential crop infections and infestations. Traditionally, the growth-stage is based upon a manual random sample of a very small number of plants that are then analyzed to produce an estimate for the entire crop (up to thousands of acres). In order to increase the sample size (and thus accuracy) and to enable precision agriculture to address non-uniform crop development across a field, we present an analysis methodology that facilitates the automated growth-stage analysis of dense point clouds that are derived from drone imagery. Our method utilizes a standard camera drone and does not use specialized sensors or geo-spatial tagging. We propose a multi-stage unsupervised method, which provides information about the individual plant locations in a field plot with a high probability. The method also produces a measure of individual plant heights, which along with their location are critical for later growth-stage estimation and necessary for robotic precision application. We confirm our method's efficacy with experimental results on corn fields in Minnesota.

Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse environments. By utilizing domain randomization while training in simulation, a robust control policy can be learned that transfers well to the real world. In this article, we implement autonomous set point navigation on a tumbling robot prototype and evaluate it on flat, uneven, and valley–hill terrain. Our results demonstrate that RL-based control policies can generalize well to challenging environments that were not encountered during training. The flexibility of our system demonstrates the viability of nontraditional robots for navigational tasks.

In view planning, the position and orientation of the cameras have been a major contributing factor to the quality of the resulting 3D model. In applications such as precision agriculture, a dense and accurate reconstruction must be obtained quickly while the data is still actionable. Instead of using an arbitrarily large number of images taken from every possible position and orientation in order to cover the desired area of study, a more optimal approach is required. We present an efficient and realistic pipeline, which aims to optimize the positioning of cameras and hence the quality of the 3D reconstruction of a field of row crops. This is achieved with four steps; an initial flight to obtain a sparse point cloud, the fitting of a simple mesh model, the planning of images via a discrete optimization process, and a second flight to obtain the final reconstruction. We demonstrate the effectiveness of our method by comparing it with baseline methods commonly used for agricultural data collection and processing.