# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### Simultaneous Localization and Mapping (SLAM)  
SLAM involves creating a map for an unknown environment while simultaneously determining an agent's location within it [108]. First, in landmark extraction, features of the surrounding environment are identified and mapped in relation to one another. For example, a laser scanner mounted on a mobile agent such as a robot or piece of equipment obtains a range of measurements about the surroundings to determine distances between landmarks; these landmarks are used simultaneously to localize the agent within that environment. In data association, different scans from landmark  
Figure 6: Inertial Navigation Systems (INS) in the forest. The forester’s coordinates are determined by GNSS at the first position (P1). The INS in his device tracks the direction and speed of his movement and can calculate his second position (P2) in the dense canopy based on where the forester has traveled from his last known GNSS coordinate. extraction are matched to increase knowledge of the mobile agent's position. The agent's state in the space is estimated through combined landmark observations and recorded odometry data and then continuously updated, forming a real-time map [108]. While this type of localization works best indoors, it has been deployed in forestry experimentally. For example, [PERSON] et al. used SLAM with Google Tango to extract tree diameter measurements and stem curve information for forest inventory [109], and SLAM techniques have also been paired with analysis of Light Detection and Ranging (LiDAR) data for high accuracy stem mapping (Figure 7) [7]. It is important to note that SLAM is defined as a concept rather than a single algorithm [108]. SLAM techniques are capable of sub-meter accuracy in forest environments. For example, using Google Tango, which employs color and depth cameras (RGB-D), inertial sensors, and SLAM, [PERSON] et al. demonstrated root mean square errors (RMSE) of 0.2 to 1 m under the canopy [110]. [PERSON] et al. reported similar accuracies for SLAM combined with an inertial measurement unit (IMU) in a small-footprint LiDAR, with RMSE values as low as 0.16 m in dense forest conditions [6]. The [PERSON] et al. study observed slightly higher errors (1.73 m) in open forests, however, which have fewer features for landmark extraction. SLAM may be integrated with GNSS/INS for even greater accuracy. [PERSON] et al. reported horizontal positioning accuracy of 6 cm when incorporating bearing and velocity data from GNSS/INS systems, representing an 86% improvement from using GNSS/INS alone [7]. While SLAM is used widely with robots and is of interest in development of autonomous forest machines equipped with environmental sensory recognition such as LiDAR, machine vision, or acoustic sensors, the concept of _map-matching_ is a related concept that may not necessarily require direct sensory recognition of the local, surrounding environment to aid navigation. Rather, in map matching, an existing map, such as navigational applications (e.g., Google Maps) are used as the basis  
Figure 7: Simultaneous Location and Mapping (SLAM) techniques in the forest (based on [PERSON] et al.) [7]. First, reference trees with known diameters must be mapped with their GNSS coordinates (not shown). Then, mobile laser scanning equipment (e.g., LiDAR) scans an area of interest in the woods (**left**). From the LiDAR point cloud data (**bottom right**), a stem distribution map is generated (middle right), which is compared to the reference tree map (**top right**) and in turn used to determine position. In practice, certainty in the map and location estimates improves with repeated passes and multiple perspectives. for estimating the location or trajectory of individuals or pieces of equipment locally based on (1) known, approximate location (from a GNSS receiver, for example), and (2) the location and heading of that position relative to known map features such as roads or trails [111; 112].