Slope Stability 2011: International Symposium on Rock Slope Stability in Open Pit Mining and Civil Engineering, Vancouver, Canada (September 18‐21, 2011) 

 

Accurate LIDAR Change Detection for Slope Stability and Rockfall Monitoring

J. Kemeny University of Arizona, Tucson, Arizona, USA

J. Handy Split Engineering, Tucson, Arizona, USA

D. Kraemer Split Engineering, Tucson, Arizona, USA

B. Norton Split Engineering, Tucson, Arizona, USA

Abstract Ground-Based LIDAR is a new technology for the remote sensing of rock slopes. One proven application of ground-based LIDAR is rock mass characterization, which involves the accurate characterization of discontinuity orientation, spacing, roughness and other information. A second important application of ground-based LIDAR is change detection, where point clouds from successive scans of the same scene are subtracted to produce a difference point cloud. This paper describes the results of three case studies that have been conducted to evaluate the accuracy of LIDAR change detection. The first case study involved small artificial movements of jersey and plastic barriers that were scanned at distances of 110 and 286 meters. The second case study involves small artificial movements of small rock boulders. The third case study involves monitoring rockfall over a 9 month time period along a rockfall-prone section of Highway 285 in Colorado. Routines have been implemented to accurately align point clouds using an Iterative Closest Point (ICP) algorithm, construct difference point clouds, and visualize the results in various ways. The results show that movements as small as 5 mm can be detected at a range of 110 meters. Also, an algorithm has been developed to detect and outline “regions of change” that represent displaced rock blocks or soil mass areas.

1 Introduction Terrestrial LIDAR (also referred to as Terrestrial Laser Scanning and Ground-Based LIDAR) is a new technology for capturing and visualizing three-dimensional data. The output of a LIDAR scan is a “point cloud” consisting of millions of points that represent the topographic surface that was scanned. Measurements and calculations can be made from the point cloud itself, or from a triangulated surface produced from the point cloud. Point cloud processing software refers to software specifically designed to process point clouds from LIDAR scans. LIDAR scanning and point cloud processing software are now routinely being used in a number of engineering and architectural fields (Sparpoint, 2010).

Terrestrial LIDAR is ideal for many geotechnical applications, including surface and underground rock mass characterization, surface slope stability, underground ground control, rockfall, and displacement monitoring. LIDAR scanning collects data from a distance, and thus increases the safety associated with data collection in unstable ground conditions. It is also able to collect data from areas where normal access would be difficult or impossible. LIDAR data is high resolution and eliminates many of the human bias and low-resolution issues with hand-collected data. Finally, LIDAR scanning and point cloud processing is very fast and allows for the characterization of a site in a timely fashion. Details on the use of terrestrial LIDAR for geotechnical applications are described in Kemeny & Turner (2008).

LIDAR point clouds provide a detailed topography of scanned regions, and ground movements are analyzed by taking a sequence of scans of the same region at different times, referred to as change detection. LIDAR change detection involves taking a “before” and “after” point cloud and subtracting the two to create a “difference” point cloud. LIDAR change detection can detect fairly small ground movements, even though not as small as

some other technologies such as traditional surveying or slope radar (ClimChAlp, 2008; Groundprobe, 2011). However, ground-based LIDAR has a significant advantage in that it captures the 3D topography with a very high spatial density and resolution. LIDAR change detection should be at least as accurate as the combined error from each of the two point clouds used to make the difference point cloud. Thus, if the accuracy of each point cloud is 2-6 mm a scan distance of 100 m, a typical accuracy range for high-resolution scanners on the market today, then changes greater than 4-12 mm should be able to be detected. However, point cloud alignment is a critical step in the change detection process. If the scanner has been moved between the before and after scans, then the two point clouds need to be accurately aligned before the difference point cloud is produced. Surveying can be used to align the point clouds, even though surveying alone does not generally provide enough accuracy to achieve the numbers given above. If most of the scene has not changed between the before and after scans, then a technique called Iterative Closest Point (ICP) can be used to accurately align the before and after point clouds. ICP involves small rotations and translations of the point clouds to minimize the distance between the points of the two clouds. A number of variations of ICP algorithms have been developed, as described in Bae & Lichti (2008) and ICP (2011).

This paper describes several case studies that have been conducted to assess the accuracy of LIDAR change detection. We have implemented routines to accurately align point clouds using an ICP algorithm, construct difference point clouds, and visualize the results in various ways. The results show that movements as small as 5 mm can be detected at a range of 110 meters. Also, an algorithm has been developed to detect and outline “regions of change” that represent displaced rock blocks or soil mass areas. This paper also discusses some remaining issues with difference point clouds and strategies to fix these problems.

2 Case studies Three case studies were used to assess the accuracy of LIDAR change detection, as described below.

2.1 Site 1: CDOT facility near Empire, Colorado

The first site is located in the CDOT Storage Facility near Empire, Colorado. The test site is a manmade wall consisting of jersey barriers, plastic barriers, some thin cement sheets, and some round concrete blocks. Pictures of the site are shown in Figure 1. The site is meant to simulate a rock slope. Small displacements were made to the plastic barriers, cement sheets and the concrete blocks, and “before” and “after” LIDAR scans were made to determine if these small displacements could be detected.

Figure 1. a) Site 1 near Empire, Colorado, consisting of jersey and plastic barriers, b) Optec ILRIS 3D

scanning of the site from the distance of 286 meters.

Baseline scans were initially made at distances of 110 and 286 meters from the wall. Movements in the range of 3.8 mm to 10.6 cm (0.15 – 4 inches) were then made in the barriers, sheets and blocks, and rescans were made at both distances. Details of the scans are given in Table 1. This paper discusses only “movement 1” at the Empire site even though a second set of movements were also made.

Table 1. Details of the scans described in this paper.

Site Location Date Scanner Event # Points (after cropping) Site 1 - CDOT Storage Facility near Empire, Co

110 m from target June 7, 2010 ILRIS-3D Before movement 1 56,675 286 m from target June 7, 2010 ILRIS-3D Before movement 1 22,181 110 m from target June 7, 2010 ILRIS-3D After movement 1 33,005 286 m from target June 7, 2010 ILRIS-3D After movement 1 80,492

Site 2 – Tucson, AZ

backyard May 12, 2010 Split – W1 Before movements 397,025 backyard May 12, 2010 Split – W1 After movements 647,747

Site 3- US 285, mile marker 246.7

Right side of slope Sept. 9, 2009 ILRIS-3D Before scan 1,779,339 Left side of slope Sept. 9, 2009 ILRIS-3D Before scan 1,316,635 Right side of slope June 15, 2010 ILRIS-3D After scan 2,393,937 Left side of slope June 15, 2010 ILRIS-3D After scan 2,130,097

2.2. Site 2: Backyard in Tucson, AZ

The second field site was the backyard of the software developer for the change detection software (Jeff Handy) in Tucson, Arizona. This site had a variety of objects, including brick walls, plants, patio furniture, and soil and rock. This site had access to electrical power, and this site also allowed the software developer to easily modify and calibrate the software. A number of movements of small rock boulders and other objects were made, and before and after scans were made. Details of the scans are given in Table 1, and a point cloud from the site is shown in Figure 2. These scans were conducted using a low-cost scanner (parts less than $12,500, referred to as Split W1) that was built based on a paper by Willis (2009). This paper discusses only one set of before and after scans even though scans were continually captured at this site for a period of several weeks.

Figure 2. Grayscale point cloud from Site 2 in Tucson, Arizona.

2.3 Site 3: US 285 in Colorado

The third site is along US 285, mile marker 246.7, approximately 20 miles from downtown Denver, Colorado. This is a divided four-lane highway, and the cut slope that was scanned is approximately 420 feet in length and a maximum height of about 60 feet. The average grade of the cut slope is 60 degrees. The ditch width varies

between 9 and 12 feet and concrete barriers have been installed along the edge of the ditch. Pictures of the site are shown in Figure 3.

Figure 3. Site 3 along with the scanner used, an Optec ILRIS3D.

The rock slope at this site consists of angular boulders dispersed within a fine matrix. A historic landslide created this slope. Weathering over time causes the angular boulders to protrude from the slope, loosen, and finally roll down the slope. The ditch and jersey barriers contain most of the rockfall, but in the past few years several large boulders have rolled onto the west-bound lanes of US 285, as in the 2007 event shown in Figure 4.

Figure 4. Large rockfall along US 285 in Colorado that occurred in April 2007.

Original LIDAR scans of the slope were taken September 9, 2009 and rescanning of the same slope was made on June 15, 2010. Details of the scans are given in Table 1. In the time period between the scans several boulders did become dislodged from the slope and were captured by the LIDAR scanning, as discussed in Section 3.

3 Change detection LIDAR change detection involves taking the point clouds from “before” and “after” scans and creating a “difference” point cloud. The difference cloud is created by subtracting the after cloud from the before cloud. Before the subtraction takes place the two point clouds must be accurately aligned. This is a two-step process. In the first step markers are manually inserted in the before and after clouds (normally 3 markers are used) to give a crude alignment. This step is not necessary if the scanner is not moved between the before and after scans. In the second step the clouds are accurately aligned using an Iterative Closest Point (ICP) algorithm. Also, before subtraction takes place the clouds are smoothed using a smoothing filter. The subtraction itself is complicated since the points in the two scans will not line up with each other. Thus for a point in one cloud, the equivalent point in the other cloud is determined using interpolation. Once the difference cloud is created, it can be visualized in different ways. Normally change that involves movement away from the scanner (such as a missing rock on the slope) is given one color (red in our case), change that involves movement towards the scanner (such as new rock in a ditch) is given another color (blue in our case) and movement less than the noise level is given a third color (gray in our case). Cross sections through the difference point cloud can also be made to look at detailed movements. Change detection results from the three case studies are given below.

3.1 Site 1: CDOT facility near Empire, Colorado

First the results are shown for scans taken at a distance of 110 meters from the artificial rock wall. The wall is broken up into 12 segments labeled A through L as shown in Figure 5b, and the actual displacements in movement 1 as made by Ty Ortiz are given in Figure 5a. Figure 5c shows the difference point cloud from the Otech ILRIS-3D scans from a distance of 110 meters. Movement toward the scanner is shown in blue, movement away from the scanner is shown in red, and the threshold noise level for this difference cloud is about 11 mm (0.43 inches, shown in gray). Comparing Figure 5a and 5c, the blue patches agree with the actual displacements for all regions where a displacement greater than 10.2 mm (0.4 inches) was made (B2, C1, C2, D1, F2, H1, H2, I1, I2, K, L). Block J is red because it was removed between the scans.

a)

Figure 5. a) Actual displacements made to the artificial wall. (See next page for continuation).

b) 

c) 

d)

e)

f) Figure 5. (Continued). b) Artificial wall with reference numbers, c) difference point cloud using Optech

scanner at a distance of 110 meters, d) differences along upper barriers, e) differences along middle barriers, and f) difference point cloud at a distance of 286 meters.

Figures 5d and 5e show cross sections through the difference point cloud through the top and middle plastic barriers, respectively. They provide additional details and show that the LIDAR scans taken from a distance of 110 meters are capable of detecting change as small as 5.1 mm (0.2 inches). (see G1 and G2 locations in Figure 5e compared with actual movements given in Figure 5a). Some of the waviness in these cross sections is due to the corrugations in the plastic barriers.

The results of the movements taken from the 286 meter distance with the Optech ILRIS-3D scanner are shown in Figure 5f. It is not as accurate as Figure 5c because of the increased distance. The noise level for the difference point cloud shown in Figure 5b is 1.4 cm compared to 1.1 cm in Figure 5c. Analysis of cross sections through this point cloud (not shown) indicate that the LIDAR scans taken from a distance of 286 meters are capable of detecting change as small as 15.2 mm (0.6 inches).

3.2 Site 2: Backyard in Tucson, Arizona

In addition to developing an ICP algorithm to accurately align before and after scans, and developing software to produce and visualize difference point clouds, software was developed to detect and delineate “regions of change”. A region of change could represent a displaced rock block or a moving area within a rock mass or soil mass. Regions of change are grouped based on an algorithm developed that looks at neighboring points in the difference point cloud, and these regions of change are then automatically circled in the corresponding point clouds, a red circle for the original location of a rock block and a blue circle for the end location. Tracking can then be conducted between the original and final location of a rock block to determine particle displacement.

Figure 6a shows a difference point cloud and Figures 6b and 6c show before and after point clouds for Site 2, where a number of rock blocks were moved between scans, with movements ranging from 0.5 to 11 inches. The movements have been automatically detected and outlined in Figure 6a, indicating that the boulders on the left were moved back while the boulders on the right were moved forward. The red and blue outlines in the before and after point clouds, respectively, automatically track the movements of the rock blocks and agree with the actual movements that were made. The accuracy of the change detection was very good, with gray (noise) assigned to any change less than 3.8 mm (0.15 inches).

Figure 6. Change detection example from the 2nd field site. a) Difference point cloud, red indicating positive change, blue indicating negative change and gray indicating change less than a threshold noise value (±0.15 inches in this case), b) red outlines superimposed on the original point cloud indicating “before” position, c) blue outlines superimposed on the final point cloud indicating “after” position.

3.3 Site 3: US 285 in Colorado

The results from before (September 2009) and after (June 2010) scans taken at Site 3 are shown in Figure 7. Figure 7a and 7b show before and after photos, indicating a large missing boulder on the left side and other changes. Figure 7c shows the difference point cloud of the left and middle parts of the slope. Red indicates missing material and gray indicates movement less than the noise level. Because of the numerous small changes that occurred to the soil matrix in the nine months between the before and after scans, the background noise level is high in this case with a value of over 10 inches. Thus only rockfall events with a size greater than 10 inches are shown in Figure 7c. Figure 7c clearly shows several large rockfall events that have occurred, as well as a sign in the lower right that appears to have been bent at some point in the nine-month interval.

Figure 7. Before (September 2009) and after (June 2010) pictures and difference cloud along Highway 285 in Colorado.

4 Conclusions This paper has described the results of three case studies that have been conducted to evaluate the accuracy of LIDAR change detection. The first case study involved small artificial movements of jersey and plastic barriers that were scanned at distances of 110 and 286 meters. The results indicated that movements as small as small as 5 mm from 110 meters and 15 mm from 286 meters could be detected. These results are from a 10-year old scanner with an accuracy of about 5 mm, and better results can be expected with some of the newer scanners. The second case study involved small artificial movements of small rock boulders scanned from a distance of

about 12 meters. The results indicated that movements as small as 3.8 mm could be detected. These results were also used to validate software that has been developed to delineate and track the movement of “regions of change” such as rock blocks or areas in a soil mass. The third case study involved before and after scans along a rockfall prone section of Highway 285 in Colorado. Before and after scans were taken 9 months apart and clearly show the movement of several large rock blocks.

There are still some remaining issues associated with using difference point clouds to monitor ground movement. In many cases small ground movement may result in difference point clouds with “ground movement artifacts”. In a small block translation, for instance, missing material (negative change) is detected at the back of the translated object and new material (positive change) is detected at the front of the translated object. These are artifacts that could be mistaken for a smaller object being translated by a larger amount. A similar situation occurs for small block rotations. The shapes of the positive and negative change provide some evidence that artifacts are occurring. In the case of a translated elliptical object, for instance, the change artifacts will have opposing crescent shapes, as shown in Figure 8a. In the case of block rotations, the evidence that artifacts are occurring are that blocks appear to be moving sideways or uphill rather than down the slope, as shown in Figure 8b. In the future these and additional criteria will be developed and tested to differentiate ground movement from artifacts.

Figure 8. Examples of small particle translations and rotations that result in artifacts, red is missing material,

blue is new material, gray is no change (less than error threshold). a) opposing crescent shapes due to translation, b) particles moving in all directions (including uphill) due to rotation

5 Acknowledgements Support is acknowledged from grants 0945294 and 0653942 from the National Science Foundation, and Transportation Pooled Fund grant TPF-5(166) administered by the Arizona Department of Transportation. Special thanks to Ty Ortiz from the Colorado Department of Transportation for assistance in the case studies conducted in Colorado.

6 References Bae, K., Lichti, D. (2008). A method for automated registration of unorganised point clouds. ISPRS Journal of

Photogrammetry & Remote Sensing 63: 36–54. ClimChAlp (2008). Slope Monitoring Methods: A State of the Art Report. Strategic Interreg III B Project ClimChAlp

Report, published by The ClimChAlp partnership, Munich, Germany. GroundProbe (2009). Slope radar system. (http://www.groundprobe.com/). ICP (2011). Iterative Closest Point. Wikipedia (http://en.wikipedia.org/wiki/Iterative_Closest_Point). Kemeny, J., Turner, K. (2008). Ground-Based LiDAR: Rock Slope Mapping and Assessment. US Dept. Transportation

Federal Highways Admin. Central Federal Lands Highway Division, Publication No. FHWACFL/TD-08-006. Sparpoint (2010). Spar Point Research LLC investigates and reports on 3D scanning, imaging and position capture

technologies (http://sparllc.com/).

Split Engineering (2010). Split-FX Point Cloud Processing Software (http://www.spliteng.com/split-fx/). Willis, A., Yungfeng, S., Ringle, W., Galor, K. (2009). Design and implementation of an inexpensive lidar scanning system

with applications in archaeology. In Beraldin et al. (eds.), Three-Dimensional Imaging Metrology, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 7239.