23 accepted papers
Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This article presents a prototype system for autonomous, undercanopy for…
Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions weaken the assumptions of traditional urban-centric localization methods, which assume that consistent features arise fr…
Forests are vital to our ecosystems, acting as carbon sinks, climate stabilizers, biodiversity centers, and wood sources. Due to their scale, monitoring and managing forests takes a lot of work. Forestry robotics offers the potential for enabling efficient and sustainable foresting practices through
Building an online 3D LiDAR mapping system that produces a detailed surface reconstruction while remaining computationally efficient is a challenging task. In this paper, we present PlanarMesh, a novel incremental, mesh-based LiDAR reconstruction system that adaptively adjusts mesh resolution to ach
Many LiDAR place recognition systems have been developed and tested specifically for urban driving scenarios. Their performance in natural environments such as forests and woodlands have been studied less closely. In this paper, we analyzed the capabilities of four different LiDAR place recognition…
For biodiversity and forestry applications, end-users desire maps of forests that are fully detailed—from the forest floor to the canopy. Terrestrial laser scanning and aerial laser scanning are accurate and increasingly mature methods for scanning the forest. However, individually they are not able…
Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing…
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to als…
Forests play a crucial role in our ecosystems, functioning as carbon sinks, climate stabilizers, biodiversity hubs, and sources of wood. By the very nature of their scale, monitoring and maintaining forests is a challenging task. Robotics in forestry can have the potential for substantial automation…
Multi-modal sensing often involves determining correspondences between each domain's signals, which in turn depends on the accurate extrinsic calibration of the sensors. Challengingly, the camera-LIDAR sensor modalities are quite dissimilar and the narrow field of view of most commercial LIDARs mean…
Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we propose Wild Visual Navigation (WVN), an online self-supervised learning system for traversability estimat…
We investigate the task of object goal navigation in unknown environments where a target object is given as a semantic label (e.g. find a couch). This task is challenging as it requires the robot to consider the semantic context in diverse settings (e.g. TVs are often nearby couches). Most of the pr…
Safe motion planning in robotics requires planning into space which has been verified to be free of obstacles. However, obtaining such environment representations using lidars is challenging by virtue of the sparsity of their depth measurements. We present a learning-aided 3D lidar reconstruction fr…
To achieve successful field autonomy, mobile robots need to freely adapt to changes in their environment. Visual navigation systems such as Visual Teach and Repeat (VT&R) often assume the space around the reference trajectory is free, but if the environment is obstructed path tracking can fail or th
State estimation is a key ingredient in most robotic systems. Often, state estimation is performed using some form of least squares minimization. Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data. Th
Accurately localizing in and mapping an environment are essential building blocks of most autonomous systems. In this paper, we present a novel approach for LiDAR odometry and mapping, focusing on improving the mapping quality and at the same time estimating the pose of the vehicle. Our approach per…
In modern agriculture, measuring phenotypic traits helps breeders monitor plant growth, increase yield, and provide food, feed, and fiber. Traditional phenotyping requires intensive manual work, partially being intrusive. In this paper, we investigate the challenge of measuring phenotypic traits in…
Plant phenotyping, i.e., the task of measuring plant traits to describe the anatomy and physiology of plants, is a central task in crop science and plant breeding. Standard methods often require intrusive or time-consuming operations involving a lot of manual labor. Cameras or range sensors, paired…
Analyzing sensor data of plants and monitoring plant performance is a central element in different agricultural robotics applications. In plant science, phenotyping refers to analyzing plant traits for monitoring growth, for describing plant properties, or characterizing the plant's overall performa…
Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate along a crop field multiple times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted interventions. In this paper, we…
Localization is a pre-requisite for most autonomous robots. For example, to carry out precision agriculture tasks effectively, a robot must be able to localize itself accurately in crop fields. The crop field environment presents unique challenges such as the highly repetitive structure of the crops…
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need…
Continuous crop monitoring is an important aspect of precision agriculture and requires the registration of sensor data over longer periods of time. Often, fields are monitored using cameras mounted on unmanned aerial vehicles (UAVs) but strong changes in the visual appearance of the growing crops a