In this paper, we propose a mobile robot localization system in frequent GPS-denied situations. We utilize multiple observations that are obtained from sequential appearance-based place recognition and GPS. Using GPS observations has still some challenging problems such as multipath or signal lost under environments where there are tall buildings nearby. The appearance-based place recognition that is combined with positional information has capability to overcome the issue. Nevertheless, GPS observations which are obtained in the situation sometimes have better quality (e.g. Precision or accuracy) than positional information from the place recognition because those coordinates always have some errors. We apply both of observations to a mobile robot localization for the sake of achieving robust localization. Moreover sequential appearance-based place recognition makes it possible to recognize their own position even when we navigate a robot at night. Our system uses not only multiple observations but also dead reckoning with the gyrodometry model. Our experiments are performed over aggregate 5300 m trajectory approximately that contains three times trials through a 1600 m outdoor route in different seasons and at different times, and once trail through a 500 m short-range route to verify its validity.

In this paper, we propose a mobile robot localization system using multiple observations, which show the robot's global position. One of observations is GPS observation, the other is utilized an appearance based place recognition. Using GPS observations has still some challenging problems such as multipath and signal lost under the environments there is tall buildings nearby. It affects a significant error on localization. On the other hand, appearance based place recognition methods are efficient to recognize the robot's global position. It becomes possible to use a scene database with global position information. However, it could fail to function properly in natural environments like a lawn grass or trees in a park. We solve these demerits of each observations by using these multiple observations. Our system uses not only multiple observations but also dead reckoning with Gyrodometry model. As a result, the proposed localization system have achieved robust localization. To verify the validity of proposed method, our experiments using 1600m outdoor course in different seasons were conducted.

This paper describes the novel road surface analysis using reflectivity of a laser scanner in structured outdoor environments. The proposed approach makes estimation of road surface conditions robust by using information of remission value as reflectivity of a laser that much less depends on brightness of color or ambient lighting than passive camera. Our method can be applied to various structured outdoor environments by online estimating distributions of the remission value from the road surface. This article shows that the method is successfully verified with accuracy of approximately 99% at both (i) the testing course of the 2009 Real World Robot Challenge which is known as “Tsukuba Challenge” and (ii) our university campus.

In this paper, we propose a technique of learning a noise pattern of visual odometry for accurate and consistent 6DOF localization. The noise model is represented by three parameters of feature points as input: (I) The number of inliers among feature points, (II) Average of distances between feature points, (III) Variance of interior angles. The correlation between these parameters and estimation error is also described. To approximate the complicate noise model accurately, our technique adopts Hybrid neural Fuzzy Inference System (HyFIS) for a learning engine. The noise model is created with HyFIS beforehand, and then the error of visual odometry is estimated by the noise model and compensated on the fly. Learning results of the noise model and results of 6DOF localization in untextured and dynamic environments are presented, effectiveness of our technique is shown.

In this paper, we propose an efficient solution to 6 degrees of freedom (6DOF) localization using unscented Kalman filter for planetary rovers. The solution is a technique augmented the unscented Kalman filter for accurate 6DOF localization, named augmented unscented Kalman filter (AUKF). The AUKF is designed to deal with problems which occur on other planets: wheel slip, visual odometry error, and gyro drift. To solve the problems, the AUKF estimates the slippage ratio in an augmented state vector, the accuracy of the visual odometry with the number of inliers among feature points, and sensor usefulness with gyrodometry model. Experimental results of rover runs over rough terrain are presented, the effectiveness of the AUKF and its each component is shown.

In this paper, we propose stereo vision based visual odometry with an effective feature sampling technique for untextured outdoor environment. In order to extract feature points in untextured condition, we divide an image into some sections and affect suitable processes for each section. This approach can also prevent concentration of feature points, and the influence with a moving object can be reduced. Robust motion estimation is attained using the framework of 3-point algorithm and RANdom SAmple Consensus (RANSAC). Moreover, the accumulation error is reduced by keyframe adjustment. We present and evaluate experimental results for our system in outdoor environment. Proposed visual odometry system can localize the robot's position within 4% error in untextured outdoor environment.

In future planetary exploration missions, robots are required to traverse over very rough terrain. On the surface of the moon there are covered with soils, which is named regolith. Regolith is easy to slide. Any stack may occur to the robots that are running on the regolith. Some researchers have studied the mobility system for robots. However the optimal and robust mobility systems have not been developed yet. For producing high traversability, this paper proposes a new wheel system, considering the characteristic of soil made a new wheel

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

In order to evaluate mobility performance of roving vehicles on natural terrain of the other planets, a number of running tests on the Earth have to be carried out in consideration of the difference of gravitational acceleration. In this paper, the authors focus on the analysis with similarity models and experimental study to take the difference of gravity into account. The authors introduce two similarity models of the planetary rovers for 1G or 1/2G gravity experiments, respectively to examine the similarity law itself and the performance of the mobility systems. This paper presents the experimental results for both low-gravity flight tests by the airplane and the tests on the ground.

This paper proposes a localization method to estimate position and azimuth of a lunar rover. In the proposed method, the position is precisely estimated by integration of an absolute and a relative position. The absolute position is measured by observing the sun and the earth, and the relative position is determined by dead reckoning. Effectiveness is confirmed by the results of simulations. Moreover, differences between proposed method and Extended Kalman Filter are shown by results of long-distance simulation.

In order to carry out experiments many times on the Earth in consideration of the difference of gravitational acceleration to design the rover properly, we introduce similarity law to design and construct the experimental models. We have produced two experimental models of the planetary rover with 5-wheel suspension system (called "PEGASUS") and 4WD system under for 1G and 1/2G gravity. We have carried out a low-gravity flight experiment in various parameters using the model on the airplane. Assuming that similarity law is true under every gravity environment, we have made relative evaluation on a degree of mobility by the difference of mobility systems. In the result of the flight experiment, it is show that PEGASUS is able to successfully move better than 4WD.

We propose a tele-operating system to achieve continuous operation such as nonstop traveling of lunar rover. In this system, the rover in the remote site synchronously moves with some constant delay from the rover in the local site. The real-time self-localizing vision system is used for position estimation of the rover and providing geographical information to an operator in the local site. By using the proposed system, the test-bed rover in the remote site can successfully run continuously without feeling influence of either time delay or bandwidth limitation of communication.

This paper describes the path planning method for a planetary microrover. A planetary rover is required to travel safely over a long distance for many days in unfamiliar terrains. Hence it is very important how a planetary rover processes sensory information in order to understand the environment and to make decisions. The authors have newly developed a small rover for future lunar or planetary exploration. As a new data structure for map information, an extended elevation map is introduced, which includes the effect of the size of the rover. The proposed path planning can be conducted in such a way as if the rover were a point while the size of the rover is automatically taken into account. The map obtained by sensors includes uncertainties. Thus a path planning scheme based on traversability and probability is also proposed. The validity of the proposed method is verified by computer simulations.

We discuss a tele-driving system for distant driving of a lunar rover, and describe our newly developed small, lightweight lunar rover called Micro-5 and its visual sensor system. The operator uses a virtual world simulator to make command path. The virtual environment of this simulator is constructed with measurement data from the rover on the moon. However, there is a communication delay between the earth and the moon. So, there is a difference between the old map data which the operator used for path planning and the new data which the rover is tracking. The operator's path command is thus less reliable to avoid obstacles and to reach the goal. Therefore, to achieve a highly reliable operator's command, we propose a command-data compensation approach which compensates this difference as the distortion of the environmental map.

We discuss a tele-driving method for long-range driving of a lunar rover. An operator uses a virtual world simulator to make a command path. The virtual environment of this simulator is constructed with measurement data from a rover on the moon. We have, however, a communication delay between the earth and the moon. So, there is a difference between old map data which the operator used for path planning and new data, which a rover is tracking on. The operator's path command has less reliability for avoiding obstacles and to reach the goal. Therefore, to make the operator's command highly reliable, we propose Command-Data Compensation, which compensates this difference as the distortion of the environmental map.

This paper presents a novel concept of self-organizing collective robots with morphogenesis in a vertical plane. It is potentially applicable to autonomous mobile robots. For physical reconfiguration of a swarm of robots against gravity, new types of mechanisms and control strategies are proposed and demonstrated. Prototype robots have been fabricated in order to confirm the basic feasibility of the mechanisms. Each robot is composed of a body and a pair of arms. The body can be regarded as a cube with edge length of 90 mm, and is equipped with permanent magnets for bonding with another robot. The arms change the bonding configuration by rotating and sliding motions. As for the control strategies, we proposed the algorithms which can locally generate specific global formations of robots, with minimum interactions between neighboring robots. The overall scheme is similar to cellular automata. The control algorithms proposed have been tested by simulations.

For evaluating the basic navigation techniques for a planetary rover on a natural terrain, long range navigation experiments had been performed at Mt. Mihara volcano of Izu-Ohshima Island in September of 1996. The experimental rover succeeded in traveling over 800 m autonomously without hitting any obstacle. This paper describes navigation functions installed in the test-bed rover. This paper also presents the results obtained during the first long range navigation test.