Danilo Cáceres Hernández

Outdoor stairway segmentation using vertical vanishing point and directional filter

In this paper we propose to detect the localization and recognition of outdoor stairway. This problem is the most fundamental step in solving the problem of autonomous stair climbing navigation. An autonomous system must be able to recognize parameters that can describe stairways in unknown environments. First, we proposed to extract the longest segments of diagonal lines from the edge image in order to identify a set of diagonal line segments candidate. This can provide information itself. Based on the vanishing point we defined the area where the stair candidate is located. We then applied the Gabor filter to detect the horizontal line. Finally, after combining the previous two steps, the algorithm defined the candidate stair area in the image. A set of stair images were used within a variety of conditions in our proposed method. As a result, testing was able to prove its effectiveness.

Simple and Efficient Method for Calibration of a Camera and 2D Laser Rangefinder

In the last few years, the integration of cameras and laser rangefinders has been applied to a lot of researches on robotics, namely autonomous navigation vehicles, and intelligent transportation systems. The system based on multiple devices usually requires the relative pose of devices for processing. Therefore, the requirement of calibration of a camera and a laser device is very important task. This paper presents a calibration method for determining the relative position and direction of a camera with respect to a laser rangefinder. The calibration method makes use of depth discontinuities of the calibration pattern, which emphasizes the beams of laser to automatically estimate the occurred position of laser scans on the calibration pattern. Laser range scans are also used for estimating corresponding 3D image points in the camera coordinates. Finally, the relative parameters between camera and laser device are discovered by using corresponding 3D points of them.

3D motion estimation based on pitch and azimuth from respective camera and laser rangefinder sensing

This paper proposes a new method to estimate the 3D motion of a vehicle based on car-like structured motion model using an omnidirectional camera and a laser rangefinder. In recent years, motion estimation using vision sensor has improved by assuming planar motion in most conventional research to reduce requirement parameters and computational cost. However, for real applications in environment of outdoor terrain, the motion does not satisfy this condition. In contrast, our proposed method uses one corresponding image point and motion orientation to estimate the vehicle motion in 3D. In order to reduce requirement parameters for speedup computational systems, the vehicle moves under car-like structured motion model assumption. The system consists of a camera and a laser rangefinder mounted on the vehicle. The laser rangefinder is used to estimate motion orientation and absolute translation of the vehicle. An omnidirectional image-based one-point correspondence is used for combining with motion orientation and absolute translation to estimate rotation components of yaw, pitch angles and three translation components of Tx, Ty, and Tz. Real experiments in sloping terrain demonstrate the accuracy of vehicle localization estimation using the proposed method. The error at the end of travel position of our method, one-point RANSAC are 1.1%, 5.1%, respectively.

Combining edge and one-point RANSAC algorithm to estimate visual odometry

In recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method.

Stairway detection based on single camera by motion stereo

In this paper we are proposing a method for detecting the localization of indoor stairways. This is a fundamental step for the implementation of autonomous stair climbing navigation and passive alarm systems intended for the blind and visually impaired. Both of these kinds of systems must be able to recognize parameters that can describe stairways in unknown environments. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. We extracted the horizontal edge of the stairs by using the Gabor Filter. From the specified set of horizontal line segments, we extracted a hypothetical set of targets by using the correlation method. Finally, we used the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing was able to prove its effectiveness.

Stairway segmentation using Gabor Filter and vanishing point

This paper we are proposing to detect the localization and recognition of an indoor stairway. This is a fundamental step in the implementing of autonomous stair climbing navigation, as well as the implementing of passive alarm systems intended for the blind and visually impaired. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. The horizontal edges of the stairs are extracted by using the Gabor Filter. Then, the vanishing point is extracted from the specified set of line segments in the aim of facilitating the reconstruction of the stair treads. After this stage, we extract a hypothetical set of targets by using the correlation method. Finally, we employ the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing is able to prove its effectiveness.

Localization estimation based on Extended Kalman filter using multiple sensors

This paper describes a method for localization estimation based on Extended Kalman filter using an omnidirectional camera and a laser rangefinder. Laser rangefinder information is used for predicting absolute motion of the vehicle. The geometric constraint of sequence pairwise omnidirectional images is used to correct the error and construct the mapping. The advantage of omnidirectional camera is a large of field-of-view, which is helpful for long distance tracking feature landmarks. For motion estimation based on vision, the absolute translation of vehicle is approximated posterior information at previous step. The structure from motion based on bearing and range sensors can yield the corrected local position at short distance of movements but it will be accumulative errors overtime. To utilize the advantages of two sensors, Extended Kalman Filter framework is applied for integrating multiple sensors for localization estimation. The experiments were carried out using an electric vehicle with the omnidirectional camera mounted on the roof and the laser device mounted on the bumper. The simulation results will demonstrate the effectiveness of this method from large field-of-view scene images of outdoor environment.

Camera and laser range finder fusion for real-time car detection

This paper describes a car detection method by combining data obtained from a laser and a camera. Data from the camera and the laser range finder (LRF) are combined after a calibration method has been performed. The calibration method defines the relative pose between camera and LRF. Car candidates are then extracted from the LRF data. The car candidate regions on the image are generated based on the filtered LRF data based on its size. To filter out the bad candidates, a verification method is performed on the car candidate regions. This method eliminates the needs of checking over several positions and scales, enables a speed enhancement over the general object detection strategy.

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance.

Smoke detection for static cameras

This paper describes the smoke detection for static cameras. The background subtraction was used to determine moving objects. Color characteristics were utilized to distinguish smoke regions and other scene members. Separate pixels were united into blobs by morphology operations and connected components labeling methods. The image is then refined by boundary roughness and edge density to decrease amount of false detections. Results of the current frame are compared to the previous one in order to check the behavior of objects in time domain.