Disclosed is a visual and spatial programming system for robot navigation and robot-IoT task authoring. Programmable mobile robots serve as binding agents to link stationary IoT devices and perform collaborative tasks. Three key elements of robot task planning (human-robot-IoT) are coherently connected with one single smartphone device. Users can perform visual task authoring in an analogous manner to the real tasks that they would like the robot to perform with using an augmented reality interface. The mobile device mediates interactions between the user, robot(s), and IoT device-oriented tasks, guiding the path planning execution with Simultaneous Localization and Mapping (SLAM) to enable robust room-scale navigation and interactive task authoring.

A method for drivable area detection and autonomous obstacle avoidance of unmanned haulage equipment in deep confined spaces is disclosed, which includes the following steps: acquiring 3D point cloud data of a roadway; computing a 2D image drivable area of the coal mine roadway; acquiring a 3D point cloud drivable area of the coal mine roadway; establishing a 2D grid map and a risk map, and performing autonomous obstacle avoidance path planning by using a particle swarm path planning method designed for deep confined roadways; and acquiring an optimal end point to be selected of a driving path by using a greedy strategy, and enabling an unmanned auxiliary haulage vehicle to drive according to the optimal end point and an optimal path. Images of a coal mine roadway are acquired actively by use of a single-camera sensor device.

In accordance with one embodiment of the present disclosure, method includes obtaining multi-level environment data corresponding to a plurality of driving environment levels, encoding the multi-level environment data at each level, extracting features from the multi-level environment data at each encoded level, fusing the extracted features from each encoded level with a spatial-temporal attention framework to generate a fused information embedding, and decoding the fused information embedding to predict driving environment information at one or more driving environment levels.

An apparatus for localizing a robot having robustness to a dynamic environment includes a map building unit which builds a map based on SLAM; a localizing unit which acquires first feature from sensor data acquired by a sensor mounted in a robot and localizes the robot using the first feature acquired from the sensor data based on the map built by the map building unit; and a map updating unit which reduces an error caused by the movement of the robot by correcting the first feature using an estimated position of the robot with regard to a feature obtained from a static object, among the first features acquired by the localizing unit.

The apparatus for detecting and removing a dynamic obstacle of a robot and the operating method thereof according to a predetermined exemplary embodiment detect and remove the dynamic obstacle while simultaneously performing the mapping and the localizing using the simultaneous localization and mapping (SLAM) technique to efficiently detect and remove a dynamic obstacle even in a situation in which a dynamic change of surrounding environment is severe and an environment to be localized is large.

An apparatus and a method for editing 3D SLAM data according to an exemplary embodiment of the present disclosure directly edits a key frame or an edge of simultaneous localization and mapping (SLAM) data by the user's manipulation, optimizes a pose graph of the 3D SLAM data based on the key frame and the edge edited by the user's manipulation, and generates a 2D grid map corresponding to the 3D SLAM data based on the updated 3D SLAM data to improve the convenience of the user for editing the 3D SLAM data.

In order to more accurately white balance an image, weightings can be determined for pixels of an image when computing an illuminant color value of the image and/or a scene. The weightings can be based at least in part on the Signal-to-Noise Ratio (SNR) of the pixels. The SNR may be actual SNR or SNR estimated from brightness levels of the pixels. SNR weighting (e.g., SNR adjustment) may reduce the effect of pixels with high noise on the computed illuminant color value. For example, one or more channel values of the illuminant color value can be determined based on the weightings and color values of the pixels. One or more color gain values can be determined based on the one or more channel values of the illuminant color value and used to white balance the image.

Systems and methods for enhanced object detection for autonomous vehicles based on field of view. An example method includes obtaining an image from an image sensor of one or more image sensors positioned about a vehicle. A field of view for the image is determined, with the field of view being associated with a vanishing line. A crop portion corresponding to the field of view is generated from the image, with a remaining portion of the image being downsampled. Information associated with detected objects depicted in the image is outputted based on a convolutional neural network, with detecting objects being based on performing a forward pass through the convolutional neural network of the crop portion and the remaining portion.

In order to achieve the above objects, according to the present invention, an information processing system includes a wind-condition estimation unit that estimates wind-condition information in a predetermined space region, and an evaluation unit that evaluates flight difficulty or economic efficiency of an aircraft based on the estimated wind-condition information. According to the present invention, an information processing method includes estimating wind-condition information in a predetermined space region, and evaluating flight difficulty or economic efficiency of an aircraft based on the estimated wind-condition information.

A method of integrity monitoring for visual odometry comprises capturing a first image at a first time epoch with stereo vision sensors, capturing a second image at a second time epoch, and extracting features from the images. A temporal feature matching process is performed to match the extracted features, using a feature mismatching limiting discriminator. A range, or depth, recovery process is performed to provide stereo feature matching between two images taken by the stereo vision sensors at the same time epoch, using a range error limiting discriminator. An outlier rejection process is performed using a modified RANSAC technique to limit feature moving events. Feature error magnitude and fault probabilities are characterized using overbounding Gaussian models. A state vector estimation process with integrity check is performed using solution separation to determine changes in rotation and translation between images, determine error statistics, detect faults, and compute protection level or integrity risk.