We present an approach to enhance wheeled planetary rover dead-reckoning localization performance by leveraging the use of zero-type constraint equations in the navigation filter. Without external aiding, inertial navigation solutions inherently exhibit cubic error growth. Furthermore, for planetary rovers that are traversing diverse types of terrain, wheel odometry is often unreliable for use in localization, due to wheel slippage. For current Mars rovers, computer vision-based approaches are generally used whenever there is a high possibility of positioning error; however, these strategies require additional computational power, energy resources, and significantly slow down the rover traverse speed. To this end, we propose a navigation approach that compensates for the high likelihood of odometry errors by providing a reliable navigation solution that leverages non-holonomic vehicle constraints as well as state-aware pseudo-measurements (e.g., zero velocity and zero angular rate) updates during periodic stops. By using this, computationally expensive visual-based corrections could be performed less often. Experimental tests that compare against GPS-based localization are used to demonstrate the accuracy of the proposed approach. The source code, post-processing scripts, and example datasets associated with the paper are published in a public repository.

Robust navigation in urban environments has received a considerable amount of both academic and commercial interest

Precision robotic pollination systems can not only fill the gap of declining natural pollinators, but can also surpass them in efficiency and uniformity, helping to feed the fastgrowing human population on Earth. This paper presents the design and ongoing development of an autonomous robot named "BrambleBee", which aims at pollinating bramble plants in a greenhouse environment. Partially inspired by the ecology and behavior of bees, BrambleBee employs state-of-the-art localization and mapping, visual perception, path planning, motion control, and manipulation techniques to create an efficient and robust autonomous pollination system.

UAVs have the potential for autonomous airborne remote sensing applications that require rapid response to natural hazards (e.g.. volcano eruptions, earthquakes). As these applications require very accurate positioning, such as airborne Synthetic Aperture Radar, tightly coupled Global Positioning System (GPS) Precise Point Positioning (PPP) Inertial Navigation Systems (INS) are an attractive method to perform real-time aircraft positioning. In particular, PPP can achieve a level of positioning accuracy that is similar to Real-Time Kinematic (RTK) GPS, without the need of a relatively close GPS reference station. However, the PPP method is known to converge to accurate positioning more slowly when compared to RTK, a drawback of PPP that is amplified whenever the receiver platform is faced with GPS challenged environments, such as poor satellite visibility and frequent phase breaks. Unfortunately, these challenging conditions occur more often when the platform being positioned is an aircraft that experiences abrupt changes in attitude. In this paper we present the use of a simulation environment to characterize the position estimation performance sensitivity of PPP/INS through a Monte Carlo analysis that is considered under various conditions, such as: the intensity of multipath errors, the number of phase breaks that occur in a flight, satellite geometry, atmospheric conditions, noise characteristics and grade of the inertial sensor, and accuracy of GPS orbit products.

An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ~≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positioning.