Relaxing the planar assumption: 3D state estimation for an autonomous surface vessel

Abstract

Autonomous Surface Vessels (ASVs) are increasingly proposed as tools to automatize environmental data collection, bathymetric mapping and shoreline monitoring. For many applications it can be assumed that the boat operates on a 2D plane. However, with the involvement of exteroceptive sensors like cameras or laser rangefinders, knowing the 3D pose of the boat becomes critical. In this paper, we formulate three different algorithms based on 3D extended Kalman filter (EKF) state estimation for ASVs localization. We compare them using field testing results with ground truth measurements, and demonstrate that the best performance is achieved with a model-based solution in combination with a complementary filter for attitude estimation. Furthermore, we present a parameter identification methodology and show that it also yields accurate results when used with inexpensive sensors. Finally, we present a long-term series (i.e., over a full year) of shoreline monitoring data sets and discuss the need for map maintenance routines based on a variant of the Iterative Closest Point (ICP) algorithm.

Coordination of Multiple Vessels Via Distributed Nonlinear Model Predictive Control

“…In this respect, the local constraints of each vessel takes into account also the description of the canal, which is similar to the one described in [8] for the road. length, mass, moment of inertia along the z axis, and damping parameters, respectively [23]. We consider three vessels (indicated in blue, black, and red in Figure 2) navigating at a canal intersection.…”

Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

“…When the control loop operating constraints and ranges are known, it is possible to tune the controller gains. Thus, given a system such as maximum and minimum errors equal to e max and e min , respectively, the maximum and minimum control actions equal to u max , and u min , respectively, the proportional controller gain k p can be given by: k p = u max − u min e max − e min = ∆u ∆e (14) where ∆u and ∆e ∈ R are the control action and error maximum ranges, respectively. Through open loop tests, the maximum torque generates a maximum angular speed of 2.32rad/s, and the minimum torque generates a null angular speed (0rad/s) [33].…”

“…Their applications in the literature are diverse, including water and port supervision [3], [4], shallow water hydrological survey [5], marine search and rescue missions [6], [7], oceanographic research [8], data collection for water quality analysis [9], spatiotemporal phenomena [10], bathymetric mapping [11], [12] and inspection of structures in bridges and platforms [13], [14], multiple vehicles formation [15], [16] as examples.…”

Accurate outdoor ground truth based on total stations

“…Regarding the robotic platforms, there are almost no limitations; the only one being how many prisms the robot can carry. Researchers used total stations to track skid steered robots [9], [10], a tethered wheeled robot [11], planetary rover [12], [13], unmanned surface vessel [14], and walking robots [4], [15]. In our application, we use a large skid-steered robot suitable for winter in subarctic forest.…”

“…Because of their long-range measurements, application on water bodies are possible. For example, Hitz et al [14] used a total station installed on the shoreline to track the position of a robotic catamaran. Total stations were also demonstrated in urban areas [16] as well as indoors [17].…”