Forestry crane posture estimation with a two‐dimensional laser scanner

Abstract

Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.

Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

“…In addition, future research focuses on improving the method by including machine learning techniques. The neighborhood shapes introduced in Section II-B could then be optimized, e.g., for SLAM [9] and forest industry applications [16]. Also, the thin and elongated road inventory items such as lighting or road sign poles might be recoverable by optimizing shape properties for the segmented areas after running connected components.…”

“…On the one hand, PRC is able to remove those points that have an abnormal range value in contrast to their neighborhood, i.e., noise and outliers that follow from reflections or background illumination [6]. On the other hand, PRC could be used to downsample, for example, ground points that are abundant in most mobile mapping scenarios, such as in urban environments [13] and in forest operations [9], [14]- [16]. This would save subsequent online computation, transmission, and postcomputation time, leading to wall-clock time savings on a larger industry scale.…”

Advances in using robots in forestry operations

“…1), which can be seen as an early application of a sensor feedback control system. Forestry machines have also been equipped with sensors in research projects to identify, for instance, the position of the crane (Hyyti et al, 2018;Lindroos et al, 2015), wheel slippage (Ringdahl et al, 2012b;Suvinen and Saarilahti, 2006), soil damage (Melander and Ritala, 2018) and machine slope (Visser and Berkett, 2015). With such information, it is possible to adapt the driving to compensate for the encountered conditions.…”

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

“…The key benefits of multi-sensor perception systems are increased availability and integrity through complementary sensing, that is, targets that cannot be detected with one sensor may be detectable with another sensor, and redundancy, that is, an observation can be cross-validated from different sources. While multi-sensor perception systems are well-known in the context of autonomous cars [6], mobile mapping [7], airborne and Unmanned Aerial Vehicle (UAV) based remote sensing [8], and robotics [9], the maritime context has received less attention. This is because research in maritime perception systems is hindered by multiple factors.…”