Feature Based Navigation for a Platform Inspection AUV

Transcription

1 Feature Based Navigation for a latform Inspection AUV ehar Tangirala* Chris Debrunner Walter Feldman Alan Fettinger Locheed Martin 00 E.7 th treet Riviera Beach, FL (56) *sehar.tangirala@lmco.com Abstract: Under the Offshore latform Inspection ystem (OI) program, an LM AUV, the MARLIN(TM), is being outfitted with a mission pacage which includes a 3D imaging sonar and processors in order to inspect and build 3D models of subsea structures, and to detect large scale damage to these structures relative to a reference model. A ey component of this model building and change detection functionality is a process by which sonar data is aligned to the reference model and the vehicle/sensor pose is recovered. An interesting by-product of this is the use of this recovered pose for feature based navigation. This paper presents a method to fuse the estimated pose from an inertial navigation system with the pose recovered from the alignment of sonar data with a reference model, and the use of this fused estimate in vehicle guidance, 3D model building and change detection, and to improve inertial navigation performance. While the technology is developed for an underwater platform inspection system, the methods have broader applicability. Results are presented to demonstrate the performance of the feature-based navigation system. Introduction Under the Offshore latform Inspection ystem (OI) program, Locheed Martin is developing and delivering post hurricane oil platform inspection capabilities using an autonomous underwater vehicle (AUV) the Marlin TM (Figure ). These inspections, initially focused on the external jacet of the platform, are carried out autonomously using a 3D imaging sonar and onboard processing and will deliver significant cost and time savings to the oil industry. The objectives of the initial offering will be to detect and localize large scale damage and anomalies; future offerings will expand on this capability. This effort leverages mature, existing capabilities from across Locheed Martin which are enhanced, customized and integrated to deliver a new capability. Figure : The Marlin AUV Anomaly, or more generally, change detection is achieved by building an in situ model of the oil platform using point cloud data from the 3D imaging sonar and comparing it with an a priori model of the same platform to detect and localize changes. The accuracy of the in situ constructed 3D model of the platform, and therefore, the accuracy of the change detection, is very sensitive to the nowledge of the sensor pose. AUV s typically navigate using an inertial navigation system (IN) that uses a variety of navigation aids including G, doppler velocity logs (DVL), acoustic positioning systems, depth sensors, and speed of sound sensors. The navigation accuracy of these INs is typically adequate for most AUV missions. This paper describes the development and implementation of a feature based navigation capability that allows the AUV to navigation

2 safely in the vicinity of an underwater platform while also increasing the accuracy of the in situ constructed 3D model of the platform and change detection. This capability is achieved by fusing the 3D sonar based pose estimate with the IN pose and using this fused pose for the guidance of the vehicle as well as to aid the IN and compensate for drifts. OI Autonomy The operational concept for the OI is to have an AUV autonomously inspect an offshore drilling platform with minimal user input - the user simply chooses the platform and specifies how much of the platform is to be inspected. The AUV autonomously plans the inspection path around the platform, executes this path to collect sonar data, builds a 3D model of the platform in real-time, and executes change detection to identify anomalies. Feedbac in the form of the path of the AUV and the detected anomalies is provided to the operator. The in situ 3D model of the platform constructed from the current inspection along with 3D models of the anomalies are available to the operator upon recovery of the AUV. These models can be exported to a variety of formats to address the needs of individual users. Future version of the OI will autonomously plan a re-visit mission to gather close up optical imagery of the sites identified as anomalous. OI brings a diverse set of capabilities from across Locheed Martin together into an integrated system which is capable of achieving the primary goal of autonomously detecting and localizing platform anomalies. OI is composed of three primary subsystems: the vehicle, autonomous perception the transformation of sensor data into information; and autonomous response which is responsible for guiding the vehicle safely through the inspection mission. The vehicle, the MARLIN TM AUV is a mature Locheed Martin product which has been used on multiple missions. LM autonomous perception technologies have been demonstrated in air using LADAR point clouds which is similar in format though not in quality to the data available from a 3D imaging sonar. LM autonomous response technologies have been applied to a wide variety of domains including land, sea surface, and air. Response and perception technologies are modified and adapted to the undersea environment to achieve the goals of OI. The integration of the major autonomy components is achieved via well defined interfaces which allow for the independent development of these ey autonomy technologies as well as the rapid insertion of plug and play capabilities. These interfaces follow the ATM F254 draft autonomy standard [] where applicable; this standard defines a messaging interface in terms of message content without any regard to transmission protocols or mechanisms. The OI uses TC/I to implement the messaging interface between the ey autonomy components. Message content, frequency, data types, and validity are all defined and maintained in the only common artifact between the three major OI subsystems. Communication between each of the subsystems is achieved through the use of a publishsubscribe message passing scheme. At the initiation of the TC/I connection between subsystems, each subscribes to the specific messages which contain information it requires. This approach limits the amount of message traffic across the networ and eliminates the need to process messages which don t contain information pertinent to the subsystem. Figure 2: OI - Major subsystem integration and maturation Initial testing and development of the integrated system is performed in a simulation laboratory which simulates vehicle dynamics, 3D sonar data, and inertial navigation data. This lab testing serves to address many integration issues early in the integration process thereby reducing

3 the need for expensive sea trials. Figure 2 illustrates the integration of the major OI subsystems and the maturation of the system through a series of test phases. Major technologies, their adaptation and integration into the OI are described next. erception: ensor-based pose estimation The perception system controls the imaging sonar sensor and processes the point clouds it produces to refine the vehicle pose relative to the prior model of the oil platform. It also integrates the point clouds in their refined poses to produce a 3D model of the oil rig which is then compared to the a priori model to detect changes. Figure 3: ensor based pose alignment As illustrated in Figure 3, the sonar point clouds are aligned to the prior model using a Random ample Consensus (RANAC) [2] approach that bears some similarity to iterative closest point methods [3]. erception: 3D model building Given the refined poses produced by the sensorbased pose alignment, the in situ 3D model is built by collecting the point cloud data in an octree structure [4], as illustrated in Figure 4. The octree stores the second order moments of the points falling in each octree cell, and also eeps trac of the empty space traversed by the ray to each point. Combining these two pieces of information, the algorithm determines whether each octree cell is occupied, empty, or unnown. The octree modeling software used in OI not only computes the structure of empty and occupied space, but also provide an extensive set of geometric queries that can be used to efficiently process the model. For example the find closest point query is used to match point cloud points to the nearest prior model point during the RANAC process. Figure 4: 3D model construction erception: Change detection To detect changes between the prior model and the newly sensed model, the models are compared on a cell-by-cell basis. Locations in which the sensed model contains occupied space and the prior model contains empty space are mared as positive changes, and locations in which the sensed model contains empty space and the prior model contains occupied space are mared as negative changes. Morphological filtering of the resulting changes and filtering based on position (e.g, to remove ground points as specified in the prior model) help to reduce false alarms. Navigation Fusion The OI system has a mid-grade inertial navigation system onboard which provides ~0.8 nautical mile per hour free-inertial performance. Very accurate vehicle pose information relative to a platform local coordinate frame is available via pose alignment which is an integral part of the in situ model building process described above. ince 3D model building and change detection performance is sensitive to the accuracy of vehicle pose, inertial pose and sensor-based pose may be fused to derive a much improved estimate of vehicle pose which is used for vehicle GNC and as an input to the pose alignment process. IN pose The OI includes a Kearfott eadevil which is an RLG based gyrocompassing IN with a heading accuracy of milli-radian. The IN is able to employ various navigation aids including a G and a Doppler velocity log. The G is used every time the vehicle is on the surface including for initial alignment of the IN. The 600 KHz Teledyne RDI DVL provides good estimates of AUV velocity relative to the sea floor if the AUV is less than 90 m from the

4 bottom. If the AUV is beyond this bottom-loc range, the DVL is only able to provide the velocity of the AUV through the water. This may be combined with nown currents to generate an estimate of AUV ground velocity, but in general, navigation accuracy is significantly affected. With ground velocity, the positional accuracy of the IN is governed primarily by scale factor (along trac) and misalignment (across trac) errors and is on the order of 0.05% of distance traveled. While this positional accuracy is adequate for many AUV missions it has a significant effect on the quality of 3D model building and change detection accuracy. The Kalman Filter on board the COT IN is not available for modification although many of its states and covariances are periodically available via an interface. Fused pose The IN pose and 3D sensor-based pose are fused as shown in Figure 5 below. use x to denote the sensor-based pose and x I to denote the IN pose. We also define an input vector: u v N, v E, v D,,, where, v N, v E, v D are vehicle velocities in the north, east, and down directions, and,, are Euler rates; all of which are available from the IN. To write the state transition equations to propagate the sensor-based pose forward in time, we assume that the vehicle moves with constant translational velocity and angular velocity given by u over the time interval [ t, t ] (of length t t t seconds) starting at the state T x. We assume that angular changes due to the angular rates over the time interval translate directly into changes in roll, pitch, and heading without interactions between the angle changes. The resulting state transition equation is: x Ax Bu ; where A = I 6x6, B = t* I 6x6, and N (0, Q ). ~ The estimate xˆ of the pose at time t which is distributed as N (, ) is projected forward to time t as Figure 5: Navigation fusion The IN pose is available at 25 Hz and the sensor-based pose is available at -5 Hz and is delayed by up to 2 seconds. To account for this delay, a history of linear and angular velocities from the IN is maintained and these are used to propagate the sensor-based pose forward to the time of the latest IN pose. The sensor-based pose valid at t is fused with the latest IN pose using a Bayesian combination. We define a pose state: x [ North, East, Down,,, ] where North, East, and Down represent the position of the vehicle in a local flat earth Cartesian frame and,, are Euler angles. We T x ˆ ~ N (, ) ; where A Bu and A A T Q At time t, when a sensor based pose estimate corresponding to some time t t is available, it is propagated forward to time t and fused with the IN pose xˆ ~ N (, ) using a Bayesian combination as: x I I I I xˆ I I I j I A Bayesian combination is used instead of a Kalman filter to avoid the problems that sometime occur when cascaded Kalman filters I xˆ

5 are used. This fused pose is utilized for the guidance, navigation and control of the vehicle. The vehicle control systems uses the fused pose for station eeping; the autonomous response subsystem uses the fused pose to determine vehicle guidance to achieve the inspection plan; and the autonomous perception subsystem uses the fused pose as an initial condition into the pose alignment process. The fused pose is also used to update the IN on a 30 second period in the form of a geodetic position fix. This period is ept high in order to avoid any potential instability in the IN Kalman filter due to a very low covariance position update. Results ose estimation and fusion filter performance were initially evaluated and tuned in the simulation laboratory. Figure 6 shows the performance of the system with a simulated initial navigation error of 0 m in the north direction. During the initial acquire behavior, the vehicle approaches the platform at a safe distance of 25 m and the perception subsystem attempts to align sonar point clouds with the a priori platform model. Once this alignment is achieved reliably, the fusion filter updates the fused pose and sends a corrected position fix to the IN. The vehicle then approaches the nominal inspection offset of 5 m and proceeds with the inspection. Figure 7-Figure 9 show that the predicted errors in pose estimation compare well with the actual errors. There is a cyclical variation in the predicted and actual errors which suggests a bias in the estimated pose. This is currently under investigation. Figure 0 shows the performance of the feature based navigation system in north position from data collected from at-sea trials off the coast of alm Beach. The north position reported by the inertial navigation system differs from the sensor-based estimate of north position by about m. This error is subsequently corrected by sending a position update to the IN. The predicted errors are very similar to the simulated case. Figure 6: ose fusion in simulation Figure 7: North errors in simulation Figure 8: East errors in simulation

6 analysis and automated cartography, Communications of the Association of Computing Machinery 24 : [3] Rusiniewicz,. & Levoy, M. (200), Efficient Variants of the IC Algorithm, International Conference on 3D Digital Imaging and Modeling. [4] amet, H.Harrison, M. A. (Ed.) (990), The design and analysis of spatial data structures. Addison Wesley. Figure 9: Down errors in simulation Figure 0: ose fusion from sea-trials - North ummary This paper presents a method to fuse highly accurate feature based pose information with pose from a COT IN to generate smooth and accurate vehicle pose estimates. This method accounts for feature based pose estimates which are asynchronous, time delayed, and available at a different sample rate from the inertial pose; and where the Kalman filter internal to the IN is not available for modification to include feature based pose measurements. imulation and sea-trials data are presented to illustrate the performance of the fusion filter. Further sea trials on subsea structures are underway at the time of this writing. References [] ATM tandard F254 (2006), tandard Guide for Unmanned Undersea Vehicles (UUV) Autonomy and Control, ATM International, West Conshohocen, A 2006, DOI: 0.520/F254-06, [2] Fischler, M. A. & Bolles, R. C. (98), Random sample consensus: a paradigm for model fitting with application to image