Barry A. Roberts

Vision-aided inertial navigation

A technique of removing inertial-odometer drift errors by identifying landmarks along the vehicles route is presented. Landmark identification is performed with the use of laser range data. The scenario being developed consists of a ground vehicle equipped for autonomous retrace of a path that was stored while the vehicle was being remotely piloted to its destination. On the outbound portion of the vehicles journey, objects are detected with the laser range imagery, tracked from frame to frame, and recorded as landmarks if they meet the requirements of the landmark model. During the retrace phase, inertial-odometer data are used to guide the vehicle along the outbound path (in reverse). The landmarks recorded on the outbound path are again located during retrace, and their locations serve to remove any positional error that has occurred. This technique of vision-aided inertial navigation is presented along with processed laser range data.<<ETX>>

Inertial navigation sensor integrated motion analysis for obstacle detection

A maximally passive approach to obstacle detection is described, and the details of an inertial sensor integrated optical flow analysis technique are discussed. The optical flow algorithm has been used to generate range samples using both synthetic data and real data (imagery and inertial navigation system information) obtained from a moving vehicle. The conditions under which the data were created/collected are described, and images illustrating the results of the major steps in the optical flow algorithm are provided.<<ETX>>

A system for obstacle detection during rotorcraft low altitude flight

An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.

Evolutionary genetics of island and mainland species of Rhagada (Gastropoda:Pulmonata) in the Pilbara Region, Western Australia

Mainland species of the camaenid genus Rhagada, endemic to northern Western Australia, have relatively large, non-overlapping geographic ranges. In contrast, over much smaller distances in the Dampier Archipelago, several locally endemic, morphologically distinctive species occur with intermingled ranges. To test alternative origins of the unusual local diversity, we compared allozymes at 21 loci in 12 archipelago populations and 14 mainland populations, representing 14 species. Genetic distances were consistently low, averaging 0.019 (range 0.000–0.051) within species, and only 0.043 (range 0.001–0.133) between species. In the Dampier Archipelago, the average genetic distance between species was even smaller (0.023). This result was indistinguishable from the within-species comparisons, highlighting the disconnection between morphological diversification and levels of molecular genetic divergence. A pattern of isolation by distance among all comparisons within the archipelago also suggests a historic cohesiveness of the species in the Dampier Archipelago. Although providing no resolution of relationships among mainland populations, a neighbour-joining tree provided further support for an in situ morphological radiation in the Dampier Archipelago, transcending variation seen over much larger distances on the mainland.

Inertial navigation sensor integrated motion analysis for autonomous vehicle navigation

Many types of existing vehicles contain an inertial navigation system (INS) that can be utilized to greatly improve the performance of motion analysis techniques and make them useful for practical military and civilian applications. This article presents the results obtained with a maximally passive system of obstacle detection for ground-based vehicles and rotorcraft. Automatic detection of these obstacles and the necessary guidance and control actions triggered by such detection will facilitate autonomous vehicle navigation. Our approach to obstacle detection employs motion analysis of imagery collected by a passive sensor during vehicle travel to generate range measurements to world points within the field of view of the sensor. The approach makes use of INS data and scene analysis results to improve interest point selection, the matching of the interest points, and the subsequent motion-based range computations, tracking, and obstacle detection. In this article, we concentrate on the results obtained using lab and outdoor imagery. The range measurements that are made by INS integrated motion analysis are compared to a limited amount of ground truth that is available.

Synergism of binocular and motion stereo for passive ranging

Range measurements to objects in the world relative to mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. An approach is presented that consists of a synergistic combination of two types of passive ranging method: binocular stereo and motion stereo. We show a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system (INS) and derive the appropriate Kalman filter to refine the estimates from these two stereo ranging techniques. We present results using laboratory images that show that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular and motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view (FOV). >

A system for obstacle detection during rotorcraft low-altitude flight

Airborne vehicles such as rotorcraft must avoid obstacles such as antennas, towers, poles, fences, tree branches, and wires strung across the flight path. The paper analyzes the requirements of an obstacle detection system for rotorcrafts in low-altitude Nap-of-the-Earth flight based on various rotorcraft motion constraints. It argues that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo in conjunction with inertial navigation system information. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.<<ETX>>An airborne vehicle such as a rotorcraft must avoid obstacles like antennas, towers, poles, fences, tree branches, and wires strung across the flight path. Automatic detection of the obstacles and generation of appropriate guidance and control actions for the vehicle to avoid these obstacles would facilitate autonomous navigation. The requirements of an obstacle detection system for rotorcraft in low-altitude Nap-of-the-Earth (NOE) flight based on various rotorcraft motion constraints is analyzed here in detail. It is argued that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors to be effective. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo. The optical flow-based motion analysis that is combined with on-board inertial navigation system (INS) to compute ranges to visible scene points is described. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.

INS integrated motion analysis for autonomous vehicle navigation

This paper presents the results obtained from a maximally passive technique of obstacle detection for ground-based vehicles and rotorcraft. Automatic detection of these obstacles and the necessary guidance and control actions triggered by such detection would facilitate autonomous vehicle navigation. The approach to obstacle detection that is presented in this paper employs motion analysis of imagery generated by a passive sensor. Motion analysis of imagery obtained during vehicle travel is used to generate range measurements to world points within the field of view of the sensor, which can then be used to provide obstacle detection. Many types of existing vehicles contain an inertial navigation system (INS) which can be utilized to greatly improve the performance of motion analysis techniques and make them useful for practical military and civilian applications. Our motion analysis approach makes use of INS data to improve interest point selection, matching of the interest points, and the subsequent motion detection, tracking, and obstacle detection. In this paper we concentrate on the results of our processing when applied to sequences of lab and outdoor imagery. The range measurements that are made by INS integrated motion analysis are compared to a limited amount of ground truth that is available.

Real-time implementation of a passive ranging algorithm

A significant need exists for automatic obstacle detection systems on-board rotorcraft due to the heavy work load demands that are imposed upon the pilot and crew. Such systems must augment the pilots ability to detect and avoid obstacles for the sake of improving flight safety. The most important requirements of obstacle detection systems include a large field-of-view, a high update/frame rate, and high spatial resolution. In military systems the requirement of covertness is also present. To satisfy the requirement of covertness Honeywell, in conjunction with NASA Ames, has developed and demonstrated through simulation the feasibility of maximally passive systems for obstacle detection and avoidance. Such systems rely on techniques of passive ranging such as motion analysis and binocular stereo to perform their function through the use of passive sensor imagery. Honeywells current efforts in passive ranging-based obstacle detection systems involves the real-time implementation of the motion analysis component of such systems. The real-time implementation within a Honeywell flexible testbed environment is the subject of this paper. An overview of the motion analysis algorithm is provided and the issues involved in its real-time implementation are addressed.

Tactical-grade dual-chip inertial sensor assembly

This paper presents a two die assembly of inertial sensors consisting of three (3) orthogonal gyroscopes and three (3) orthogonal accelerometers which demonstrates tactical-grade performance and occupies a total volume of 112 mm3. The sensor mechanisms are identical to existing tactical-grade products while the volume reductions are achieved by combining the sensors into a two-die stack that is wafer-level sealed. Noise floors less than 0.125 deg/rt-hr for gyroscopes and 0.1 fps/rt-hr for accelerometer as well as bias stabilities less than 30 deg/hr for the gyroscopes and less than 10 mg for the accelerometers are reported.