Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration.

Many robot exploration algorithms that are used to explore office, home, or outdoor environments, rely on the concept of frontier cells. Frontier cells define the border between known and unknown space. Frontier-based exploration is the process of repeatedly detecting frontiers and moving towards them, until there are no more frontiers and therefore no more unknown regions. The faster frontier cells can be detected, the more efficient exploration becomes. This paper proposes several algorithms for detecting frontiers. The first is called Naïve Active Area (NaïveAA) frontier detection and achieves frontier detection in constant time by only evaluating the cells in the active area defined by scans taken. The second algorithm is called Expanding-Wavefront Frontier Detection (EWFD) and uses frontiers from the previous timestep as a starting point for searching for frontiers in newly discovered space. The third approach is called Frontier-Tracing Frontier Detection (FTFD) and also uses the frontiers from the previous timestep as well as the endpoints of the scan, to determine the frontiers at the current timestep. Algorithms are compared to state-of-the-art algorithms such as Naïve, WFD, and WFD-INC. NaïveAA is shown to operate in constant time and therefore is suitable as a basic benchmark for frontier detection algorithms. EWFD and FTFD are found to be significantly faster than other algorithms.

Lean meat yield estimation using a prototype 3D imaging approach.

Lean Meat Yield (LMY, %) of carcass is an important industry trait, which currently is not routinely measured in Australian beef abattoirs. Objective on-line technology to determine LMY is key for wider adoption. This paper presents a proof-of-concept approach for estimating the LMY of beef carcasses from the 3D information provided by RGB-D cameras. Moreover, a specifically designed on-line data acquisition system for abattoir applications is presented, consisting of three cameras moving on a scanning rig to generate 3D carcass side reconstructions. The hindquarter is then segmented consistently across all the 3D models to extract curvature information and LMY estimated via non-linear regression based on Gaussian Process models. Sides from 119 carcasses at two different commercial abattoirs were used to evaluate this approach. Results from this preliminary study (RMSE = 3.91%, R2 = 0.69) using curvature, P8 fat and HSCW indicate that 3D imaging of beef carcasses is a viable and relatively accurate technology to estimate LMY.