A Low-Cost, Compact, Lightweight 3D Range Sensor

Raymond Sheh, Nawid Jamali, M. Waleed Kadous, Claude SammutARC Centre of Excellence for Autonomous Systems

School of Computer Science & EngineeringThe University of New South Wales

Sydney, NSW 2052, AustraliaE-mail: {rsheh,nawidj,waleed,claude}@cse.unsw.edu.au

Abstract

There are many applications that can benefitfrom the ability to acquire 3D data from en-vironmental surroundings. Examples includenavigation, mapping, localisation and robotmobility. However, in the past, the technologyfor acquiring dense, wide ranging, accurate 3Ddata was too expensive, heavy and/or powerhungry for many projects that could benefitfrom its use.In this paper, we describe a highly inte-grated 3D range scanner, based on a low-cost,lightweight Hokuyo 2D linescan LIDAR. Thissensor is almost an order of magnitude cheaperthan comparable solutions, yet produces accu-rate, dense 3D data in indoor environments. Itis also small, light and power efficient enoughto be mounted, complete with power supply, ontoy-based robots. In addition, this sensor fea-tures variable and continuously-improving res-olution, foveated sensing in the middle of itsfield of view and the ability to lock into specificangles when in 2D scanning mode to facilitateleveling on uneven terrain or to act as a verticalterrain profile scanner.

1 Introduction

3D sensing of environmental space is a fascinating areaof research, being made more widely available by im-provements in rangefinder and data processing technolo-gies. Many demonstrations of its use in a wide variety ofenvironments, such as mapping of underground spaces[Huber and Vandapel, 2003], [Thrun et al., 2003], au-tonomous robot navigation [Thrun et al., 2006], indoorenvironments [Surmann et al., 2004] and architecturalsurveying attest to its usefulness. Note that we use theterm “3D range sensor” to describe a sensor that pro-vides measurements to a number of points in 3D space(each point generally being the closest point to the sen-sor in a given direction), rather than a “true” 3D sensor

that provides a value for every “voxel” in 3D space (suchas an MRI medical imaging sensor).

We have developed a small, low-cost, lightweight 3Dsensor, based on the newly available Hokuyo URG-04LX [Hokuyo Automation, 2005] linescan LIDAR (of-ten called a laser range scanner), shown in Figure 1, forthe purpose of 3D scanning in indoor environments (theHokuyo sensor is range limited to 4.5m). Our 3D scan-ning sensor is shown mounted on the front of one of ourRedback [Sheh, 2006] robots in Figure 2 and 3. It wasdesigned with the following criteria in mind:

• Indoor use (rooms and corridors of a typical officeenvironment)

• Low cost (less than $2,000AUD)

• Low power (less than 5W)

• Light weight (less than 250g)

• Small size (capable of fitting in the front of the Red-back robot)

• Large FOV (ideally 180◦ or greater in both direc-tions)

• Robust (capable of having the Redback robot flipover it)

• Simple one-port computer interfacing

In developing this sensor, we have also been able toachieve a number of additional goals.

• Retain 2D functionality for 2D mapping and navi-gation

• Scanner leveling and rolling for functions such asterrain profile scanning

• Foveated resolution in the center of the sensor’sfield-of-view

• Variable resolution scanning and the ability to tradeoff speed for resolution on-the-fly

Figure 1: A Hokuyo URG-04LX linescan LIDAR. (Imageby Hokuyo Automation.)

Figure 2: A Redback robot in stairclimbing configura-tion. Our 3D scanning sensor can be seen mounted onthe front of the robot (right hand side of picture).

Figure 3: Our 3D sensor as mounted on our Redbackrobot. For scale reference, the base of the sensor is50mm across. Note the aluminium roll shield aroundthe Hokuyo URG-04LX linescan LIDAR which allowsthe robot to fall onto the sensor without damaging it.The Robotis AX-12 servo appears behind the LIDARand is mounted on a spring hinge mechanism, shown ac-tivated in Figure 7.

2 Background

Options for gathering environmental 3D data on an in-door or short-range outdoor basis are plentiful. However,the “low-cost, light-weight, dense indoor 3D data” nichehas been left somewhat wanting.

At one end of the scale there are extremely accuratesensors made by manufacturers such as Riegel that pro-duce professional survey quality data of buildings, con-struction sites and industrial plants. However, such sen-sors are very expensive, large, heavy and take a long timeto produce their data. More practical sensors used bymany groups instead rely on rotating linescan LIDARs,such as the SICK LMS-200 [Surmann et al., 2004]. Othertechniques [Thrun et al., 2006], [Thrun et al., 2003] makeuse of one or more of these laser range scanners, fixedto the vehicle at an angle. By making use of other tech-niques to monitor the vehicle’s motion, the position ofthe laser at each scan can be determined and compiledto form 3D data. However, such sensors are still very ex-pensive – single-laser solutions cost around $10,000AUD,weigh several kilograms and require several more kilo-grams of batteries for significant runtimes.

At the other end of the scale are numerous tech-niques, such as stereo vision, structure-from-motion andother multi-view 3D reconstruction techniques, struc-tured lighting approaches and time-of-flight range im-agers or imaging LIDARs. Whilst these approaches ben-efit from being completely solid state and, especially theformer, have the advantage of low cost and power ef-ficiency, they can be very susceptible to the lighting inthe environment in which they are used and often do notproduce dense 3D data.

Problems with camera calibration, correspondenceproblems and lighting issues in general abound. Struc-tured lighting techniques are somewhat less affected bycorrespondence issues but are generally limited to fixed,structured environments and, for general indoor envi-ronments, require a large amount of power to project asufficiently visible pattern. Time-of-flight range imagers,such as the CSEM SwissRanger or the Canesta answermany of these criticisms and we have used them withconsiderable success for robotic mapping and robot mo-bility [Kadous et al., 2006], [Sheh et al., 2006]. Howeverthey are currently still limited in accuracy, especiallydue to sensor noise and nonlinear distortions caused bylens effects. These make the automated generation of 3Dmaps somewhat difficult. These sensors are also very ex-pensive. All of these image-based sensors also have thedisadvantage of a relatively narrow field-of-view, suchthat it becomes necessary to both pan and tilt the sensorin order to map a significant portion of the environment.

3 Mechanical Design and Construction

There are a wide variety of ways in which a 2D lines-can LIDAR can be made to produce 3D data [Wulf andWagner, 2003]. They all work by sweeping the scanningplane through the desired sensing volume. We use theterm “scan” by itself to refer to the linescan LIDAR’sown line scan and the term “sweep” to refer to a rota-tion of the servo through a particular angle. Thus a “3Dscan” consists of several “scans” gathered during one ormore “sweeps”. The most common technique is to pitchor yaw the sensor as shown in Figure 4, often because itis possible to have a balanced pivot (either because thesensor’s weight is along the pivot’s axis or because thepivot is supported at both ends). Whilst this is partic-ularly important for heavy linescan LIDARs, it has twomajor drawbacks. The maximum solid-angle resolutionof this type of scanner occurs along the pivot axis. Forpitching or yawing sensors, this point is almost certainlyin areas that are not of interest, such as on either sideof the robot or directly above the robot. The seconddisadvantage is that most pitching and yawing mecha-nisms must place parts of their support structure withinthe LIDAR’s field of view. Those that avoid this needcomplex mechanics or don’t place the center of rotationin line with the LIDAR’s optical center.

An alternative arrangement is to roll the laser asshown in Figure 4. This configuration foveates the areaof highest solid-angle resolution to the center of the sen-sor’s scanning volume, which is commonly the area ofinterest. It also places all of the sensor’s mechanics inits blind spot, thus none of the sensor’s sensing abil-ity is wasted. However, such a mounting results in thefull weight of the sensor resting on the rotation bearing.By using a lightweight 2D linescan LIDAR (the HokuyoURG-04LX weighs only 160g), the strength of the pivotbearing is insignificant, allowing the pivot to both sup-port the weight of the sensor laterally and not be bal-anced on both sides. We have found that the metal sleevebearings in a standard digital servo, in our case a Robo-tis AX-12 digital servo, more than sufficient to properlysupport this sensor as long as the laser is mounted asclose to the servo as possible. Additionally, with a hori-zontal field-of-view of 240◦ the use of the Hokuyo URG-04LX linescan LIDAR allows our sensor to cover 75% ofa full spherical field of view.

A further advantage of rolling the sensor is that for arobot that may climb stairs and similar terrains, it canbe very useful to have real-time feedback on the verti-cal terrain profile. In Figure 2 for instance, knowing theheight and width of the stairs in front of the robot allowsthe angles of the flippers to be adjusted for optimal grip.Whilst rotation relative to the terrain such as stairs canbe obtained using vision, accurate vertical terrain pro-filing is best done with a linescan LIDAR mounted such

Figure 4: Three possible ways that a 2D linescan LI-DAR’s scan plane (red) may be rotated (swept) in orderto produce a 3D scan. Green poles indicate the pivotaxis. Top: Rotating about the pitch axis, highest reso-lution is along the left-right axis, lowest resolution is atthe front-to-back centerline. Middle: Rotating about theyaw axis, highest resolution is at the top, lowest resolu-tion is about the horizontal centerline. Bottom: Rotat-ing about the roll axis, highest resolution is at the front,lowest resolution is around the side-to-side centerline.

Figure 5: The sensor rotated sideways, in “terrain pro-file” mode, eg. for climbing stairs. Compare to the nor-mal state in Figure 3.

that its scan plane is vertical. By being able to rotatethe linescan LIDAR vertically as shown in Figure 5, acontinuously updated vertical terrain profile can be ob-tained. In contrast, when on flat floor, faster 2D map-ping and navigation algorithms benefit from a linescanLIDAR with its scan plane mounted horizontally. Onlya rolling mount allows the LIDAR to be placed in bothconfigurations.

The final two advantages of this configuration aresomewhat more specific to the Redback robots on whichwe have installed this sensor. The first is that whilstthe Redback is capable of leveling itself along its pitchaxis, it has no control over its roll. Combined with theability of this sensor to roll, the Redback is now ableto self-level the sensor. The second advantage is rele-vant when mounted on small, advanced mobility robotswhere space is a premium. The usual configuration ofthe laser is shown in Figure 3, where the laser avoidsobscuring the view of the cameras mounted further upon the robot. To avoid damage caused by obstacles, itis also possible to raise the sensor as shown in Figure 6.To improve this ability, metal shields were added aroundthe sensor and the whole sensor mechanism mounted ona non-actuated, spring-loaded pitch-axis pivot as shownin Figure 7. This allows the sensor to “flip up” if ittakes an impact but is tuned such that in normal opera-tion, the laser does not move around on its mount. Thusthe stress on the sensor’s bearings is reduced should therobot roll or fall onto an obstacle, as it has done severaltimes. The completed sensor is shown in Figures 2 and 3,mounted on one of our Redback robots, as used duringthe 2006 RoboCup Rescue Robot League competition.The microcontroller board is housed inside the robot.

Figure 6: The sensor turned upside down to maximiseapproach angle ground clearance. Compare to the nor-mal state in Figure 3.

Figure 7: The sensor mount, showing spring “flip back”action, designed to protect the sensor in case of impact.Compare to the normal state in Figure 3.

AtMEGA64 based Microcontroller board

Hokuyo URG-04LX linescan LIDAR

Robotis AX-12 smart servo

USB to Host PC

5V power

PowerSerial Comms

ServoPower

(mechanical link)

(separate or 5V)

Figure 8: Connection diagram for the components of our3D sensor. Note that all of the components can operateon 5VDC although it is also possible to independentlypower the servo off a higher voltage.

4 Control and Interfacing

The layout of components for our sensor appears in Fig-ure 8 and consists of three components – the HokuyoURG-04LX linescan LIDAR itself, the Robotis AX-12smart servo and a basic AtMEGA64 based microcon-troller board with an FTDI FT232 RS-232 to USB in-terface chip for host communications and a Maxim highspeed RS-232 linedriver for communication with thelaser (the Robotis servo communicates at TTL levels).The sensor as a whole interfaces to the rest of the robotvia a single USB connector through this interface chipand requires only a single 5V 1A power supply. Thefact that the microcontroller board, Hokuyo URG-04LXlinescan LIDAR and Robotis AX-12 servo only require5V to operate makes this sensor package well suited tosmall robots which may not have power rails beyond 5V.It is also possible to run the servo up to 12V for improvedperformance, in competition we power it separately fromthe unregulated battery power rails at approximately7.2V. Although the AX-12 servo is capable of continuousrotation, a reciprocating design with flexible connectingwires was chosen to avoid the cost and complexity ofsliprings. Because the sensor is symmetrical, it is onlynecessary to rotate the sensor about 180◦.

A feature of our sensor is that by default it is transpar-ent. The microcontroller firmware appears to the hostPC like the Hokuyo URG-04LX linescan LIDAR, pass-ing all recognised Hokuyo commands to the LIDAR andreturning all data transparently. In this way, the sensormay be used by software on the robot that is unaware ofits 3D scanning abilities. However, the microcontrolleralso has its own set of commands that can rotate the

sensor to a desired angle. This allows the sensor to beused as either a leveled horizontal linescan LIDAR formapping when combined with an accelerometer on therobot, or as a vertical linescan LIDAR for such tasks asclimbing stairs.

The microcontroller can also be placed into 3D scan-ning mode. As the linescan LIDAR produces data inthis mode, the microcontroller, using the synchronisa-tion pulses from the laser, stamps each set of data withthe position of the servo at the time the scan’s data wasactually acquired. Note that the AX-12 servo reportsboth the actual and commanded positions, reducing theproblems of uneven loading of the servo through its rota-tion by having the laser’s center of mass away from theaxis of rotation. By comparing positions of the servoduring adjacent scans, it becomes possible to calculatethe angular velocity of the servo and, thus, the 3D posi-tion of every hit returned from the scanner through time.Because the Hokuyo URG-04LX sensor scans at 10Hz,the sensor can rotate by a significant amount between thestart and end of a given linescan thus it becomes neces-sary to interpolate the servo’s position between readingsusing this velocity.

As it is now possible to register the position of thescanline LIDAR’s hits in 3D space regardless of theservo’s rotational speed, it is possible to add another fea-ture to this sensor. Ordinarily, the resolution of the 3Dscans produced depends on both the intrinsic resolutionof the linescan LIDAR, which is fixed at 3 points per de-gree, and on the speed at which the servo rotates. Thusthere is a tradeoff between the time taken to perform ascan and the resulting resolution of the scan. However,it is also possible to rotate the sensor quickly but repeat-edly. This takes advantage of the fact that it is possibleto calculate the exact position of each laser hit (based onthe servo velocity and timing pulses from the LIDAR)and the fact that laser hits in successive sweeps will notline up precisely (and can be guaranteed to not line upby careful selection of sweep period).

Because the Hokuyo URG-04LX has a low mass and,in particular, a low mass spinning mirror, this is possiblewithout introducing excessive strain on the bearings orsensor mountings. Thus, it is possible to produce a highresolution scan by rotating the sensor quickly to takean initial low resolution scan, then continue rotating itin both directions (in reciprocating fashion) to build upincreased resolution with each successive sweep. Withthe Hokuyo URG-04LX’s 10Hz linescan period, a rea-sonably high resolution 3D scan can be produced over aperiod of 10-20 seconds. Ideally, the sweep period wouldbe selected precisely, such that successive scans startedbetween prior scans and thus resolution would build upevenly. However, the low-cost servos used do not havesufficient command accuracy to guarantee an even dis-

tribution of hits when used in this fashion. Despite this,when mounted on a mobile robot, this has the advantageof effectively turning 3D data gathering into an “any-time” process, where as long as the robot stays still, theresolution of its data improves yet if only a short periodof time is available, some data can still be gathered.

5 Evaluation

Initial results with this sensor have proven promising.As expected, standard 2D performance of the linescanLIDAR has not been affected with the servo lockedhorizontally. Because of the lightweight nature of thecomponents and the spring loaded mounting, the servowas never found to be in danger of breaking, even inRoboCup Rescue competition conditions.

Tests of 3D performance are still in preliminaryphases, however results obtained so far have beenpromising. Figure 9 shows an example of a part of a3D scan and a photograph of the sensor in the sameposition. This 3D scan consists of data taken during asingle 20 second sweep through which the sensor rotateda little over 180◦ (as the sensor is left-right symmetri-cal, this results in full 360◦ coverage). Clearly visible isthe extremely high resolution coverage of the area im-mediately in front of the sensor, with reduced resolutionapproaching the sides.

Results from taking multiple, high speed sweeps andbuilding up a 3D scan have also been promising. Fig-ure 10 shows the evolution of a 3D scan consisting of 1,2, 3 and 5 sweeps. The results clearly demonstrate theability of the sensor to improve resolution over time andyet provide reasonable information at lower resolutions.

6 Conclusions and Further Work

There are a variety of options when it comes to 3D rangesensing. However, low-cost, light weight, high FOV,dense 3D range sensors are hard to come by. In thispaper, we have presented the design and implementa-tion of such a sensor. Our sensor is easy to interface to,with only one 5V supply and a USB connection to thehost PC and can operate transparently as a 2D linescanLIDAR without host software modifications. We havedemonstrated the abilities of our sensor, as well as theversatility that small size, light weight and low power usebrings, by mounting it onto the front of our toy-basedRedback robots, which were used in the 2006 RoboCupRescue Robot League competition.

Compared to existing scanning 3D laser rangefindersystems, our sensor is very lightweight, small and re-quires very little power. The tradeoff is a lower max-imum range of 4.5m and a slower 3D scanning speeddepending on the desired resolution. However, the lightweight nature of the sensor also allows for more rapid

Figure 9: Two views of a 3D scan taken with our sensor(top – full view, middle – detailed view), and the actuallocation (below). This set of data took 20 seconds togather and consisted of a single rotational sweep. Notethat the centerline of the sensor was pointed slightlydownwards, hence the “starburst” on the floor just aheadof the sensor. Colour indicates height in sensor co-ordinates (which are tilted slightly forward).

Figure 10: Building up a 3D scan from 1, 2, 3 and 5sweeps, showing improvement in resolution. As the posi-tions are not spaced accurately, the increase in resolutionis uneven but still contributes useful information.

rotation of the sensor and thus the option of buildingup resolution in the scan helps trade off this issue – 3Dscanning can easily become an “anytime” process. Thechoice of a rolling mount also allows the highest angu-lar resolution region of the scanned space to be placedright in front of the sensor, where it is most likely to benecessary, and allows the linescan LIDAR to be used asa conventional horizontal scanner for mapping or as aterrain profile scanner.

Compared to existing range imagery, such as stereo,structure-from-motion and structure-from-lighting, oursensor is considerably more robust to ambient lightingand other environmental variations, produces dense 3Ddata and has a very wide (240◦) field of view, althoughthere is a time trade-off due to the need to scan the envi-ronment. Although imaging LIDARs are also somewhatmore robust to ambient lighting and environmental vari-ations, they are considerably higher in cost and sufferfrom a narrower FOV.

A large FOV can be a big advantage when investi-gating techniques for 3D Simultaneous Localisation andMapping (SLAM) as correspondence issues are easier toresolve. Future work will include investigating the use ofour sensor, combined with other sensors such as an IMU,for the purpose of 3D SLAM to determine how the largerFOV can be of use, and the effect of potentially varyingresolution scans on the quality of matching. We wouldalso like to investigate the combination of data from the3D sensor and the omnidirectional camera aboard theRedback robot. Although low in resolution, it is an-ticipated that the omnidirectional camera can assist inproviding improved human readability to the 3D mapsand may also be useful in resolving correspondence issues

when used with 3D SLAM.

Acknowledgements

We would like to acknowledge the Australian ResearchCouncil Centre of Excellence for Autonomous Systemsfor funding this work. We would also like to thank DavidJohnson for producing the microcontroller boards andour fellow “Team CASualty” RoboCup Rescue RobotLeague team members for their support.

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