LIDAR and monocular based overhanging obstacle detection

This paper presents an improved method for the detection of obstacles in the trajectory of autonomous ground vehicle (AGV). The novel approach requires fewer calculations, i.e. less computational time. Obstacle detection algorithms were investigated, in order to perform safe motion control, in an environment with unknown overhanging obstacles. We describe a two dimensional (2D) laser sensor application, and optimal sensor configurations for mounting a monocular camera to monitor path ahead clearance. Two different sensors are used, a vision sensor and a scanning laser, Light Detection and Ranging (LIDAR). While LIDAR measures the precise distance to the object, it cannot detect low objects and overhanging obstacles due to its predefined, constant, scanning height and angle. In contrast, vision sensor provides 2D scenery information with relatively poor distance information. To compensate for the drawbacks of these two sensors, the sensor fusion method for obstacle detection of AGV is proposed. Size expansion cue algorithm is deployed to achieve that goal. Proposed method is validated experimentally

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain. To this end, we present TerrainNet, a vision-based terrain perception system for semantic and geometric terrain prediction for aggressive, off-road navigation. The approach relies on several key insights and practical considerations for achieving reliable terrain modeling. The network includes a multi-headed output representation to capture fine- and coarse-grained terrain features necessary for estimating traversability. Accurate depth estimation is achieved using self-supervised depth completion with multi-view RGB and stereo inputs. Requirements for real-time performance and fast inference speeds are met using efficient, learned image feature projections. Furthermore, the model is trained on a large-scale, real-world off-road dataset collected across a variety of diverse outdoor environments. We show how TerrainNet can also be used for costmap prediction and provide a detailed framework for integration into a planning module. We demonstrate the performance of TerrainNet through extensive comparison to current state-of-the-art baselines for camera-only scene prediction. Finally, we showcase the effectiveness of integrating TerrainNet within a complete autonomous-driving stack by conducting a real-world vehicle test in a challenging off-road scenario

Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications

For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development

Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair

 Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.   This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchair’s pathway based on the line shape of reflected line laser. Obstacle’s distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios