Characterization of the Hokuyo URG-04LX laser rangefinder for mobile robot obstacle negotiation

Abstract

This paper presents a characterization study of the Hokuyo URG-04LX scanning laser rangefinder (LRF). The Hokuyo LRF is similar in function to the Sick LRF, which has been the de-facto standard range sensor for mobile robot obstacle avoidance and mapping applications for the last decade. Problems with the Sick LRF are its relatively large size, weight, and power consumption, allowing its use only on relatively large mobile robots. The Hokuyo LRF is substantially smaller, lighter, and consumes less power, and is therefore more suitable for small mobile robots. The question is whether it performs just as well as the Sick LRF in typical mobile robot applications.In 2002, two of the authors of the present paper published a characterization study of the Sick LRF. For the present paper we used the exact same test apparatus and test procedures as we did in the 2002 paper, but this time to characterize the Hokuyo LRF. As a result, we are in the unique position of being able to provide not only a detailed characterization study of the Hokuyo LRF, but also to compare the Hokuyo LRF with the Sick LRF under identical test conditions. Among the tested characteristics are sensitivity to a variety of target surface properties and incidence angles, which may potentially affect the sensing performance. We also discuss the performance of the Hokuyo LRF with regard to the mixed pixels problem associated with LRFs. Lastly, the present paper provides a calibration model for improving the accuracy of the Hokuyo LRF.

Hokuyo URG Series Block in Matlab Simulink

“…3, No. 6, November 2014 performance and characteristic of Hokuyo URG-04LX-UG01 is almost the same with the performance of URG-04LX that has been reviewed its characteristics by [6], [7]. They said, URG-04LX output performance need 90 minutes to stable.…”

Lidar-Based Relative Position Estimation and Tracking for Multi-robot Systems

“…3). After deletion of isolated points caused by a mixed pixel phenomenon [10], which generates a measured range resulting from a combination of the foreground and the background objects, we cluster the scan points using simple nearest neighbor classification by moving a sliding window of a 3-point size and discerning the separated objects using a minimal Euclidean distance threshold. The thresholds are highly implementation dependent, in our case are T sec = 20, T max sel = 200 and T min sel = 20.…”

Performance Evaluations on UAV-Aided Automated Meter Reading

“…Table 3 shows the results the indoor experimental studies conducted using the autonomous hexarotor shown in Figure 7. Observations were gathered using the onboard laser range finder, Hokuyo UR04-LX [21] Considering the results of the experiments on ATE, it can be concluded that a UAV can navigate and localize successfully using its onboard sensors and a preloaded map when collecting metering data in rural areas. Also, as shown in Table 3, it is observed that increasing the UAV's speed increases ATE and reduces accuracy.…”