Method of rut detection using lasers and in-vehicle stereo camera

Kage, Toshiki; Matsushima, Kousuke

doi:10.1109/iciibms.2015.7439479

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Terrestrial laser scanning has also been used successfully in many other applications ranging from sediment deposition and erosion (Stenberg et al 2016) to mapping landslides (Jaboyedoff et al 2012) and estimating forest stand characteristics (Liang et al 2016). Mounting a LiDAR sensor to a moving vehicle has been reported by Ordonez et al (2011), Laurent et al (2012, Hyyti and Visala (2013) and Kage and Matsushima (2015). Until recently this has been considered too costly for rut depth extraction in timber harvesting.…”

Section: Introductionmentioning

confidence: 99%

Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?

Salmivaara

Miettinen²,

Finér

et al. 2018

International Journal of Forest Engineering

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Soil rutting caused by forest operations has negative economic and ecological effects and thus limits for rutting are set by forest laws and sustainability criteria. Extensive data on rut depths are necessary for post-harvest quality control and development of models that link environmental conditions to rut formation. This study explored the use of a Light Detection and Ranging (LiDAR) sensor mounted on a forest harvester and forwarder to measure rut depths in real harvesting conditions in Southern Finland. LiDAR-derived rut depths were compared to manually measured rut depths. The results showed that at 10-20 m spatial resolution, the LiDAR method can provide unbiased estimates of rut depth with root mean square error (RMSE) < 3.5 cm compared to the manual rut depth measurements. The results suggest that a LiDAR sensor mounted on a forest vehicle can in future provide a viable method for the large-scale collection of rut depth data as part of normal forestry operations.

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Section: Introductionmentioning

confidence: 99%

Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?

Salmivaara

Miettinen²,

Finér

et al. 2018

International Journal of Forest Engineering

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“…Improvements in vision techniques allow for more accurate assessments of rutting than those previously obtained. To increase the measurement accuracy, Kage and Matsushima [16] used four lasers and an in-vehicle binocular stereo to measure three-dimensional rutting information. Similarly, Kenarsari et al [17] designed a three-camera structure to assess the rutting depth, area, and volume.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Simulated Lunar Soil Information Using Rutting Images

Gao¹,

Zhou²,

Jiang³

et al. 2020

IEEE Access

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Research on lunar soil has great value, and soil hardness is one of the essential parameters to be measured. Currently, the existing method involves contact measurement using special instruments. To reduce the self-weight and increase the scientific payloads of lunar rovers, reanalysis of the data obtained from existing sensors provides a great solution for achieving different tasks. Therefore, a vision-based and noncontact method via rutting images is proposed to estimate soil hardness. The proposed approach generally includes two parts: rutting depth measurement and soil hardness measurement. To simulate a complex lunar environment, a variety of simulated lunar soil preparation schemes to achieve different soil states are designed in this paper. More importantly, this paper summarizes the empirical formulas of the simulated lunar soil in different states and the judgment conditions for defining the soil states based on the rutting depth. Finally, the accuracy, feasibility, and applicability of this method are verified by experiments, which demonstrate the capability of the proposed method to effectively estimate soil hardness in simulated environments, as well as in the real lunar environment.

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