Obstacle Detection Approach for Robotic Wheelchair Navigation

2020 ◽  
pp. 261-268
Author(s):  
Devendra Somwanshi ◽  
Mahesh Bundele
Keyword(s):  
Obstacle Detection ◽  
Detection Approach ◽  
Robotic Wheelchair

Negative Obstacle Detection Using LiDAR Sensors for a Robotic Wheelchair

2018 ◽  
Author(s):  
Taylor E. Baum ◽  
Kelilah L. Wolkowicz ◽  
Joseph P. Chobot ◽  
Sean N. Brennan
Keyword(s):  
Time Algorithm ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Detection Accuracy ◽  
Time Varying ◽  
Wheelchair Users ◽  
Development Platform ◽  
Robotic Wheelchairs ◽  
Robotic Wheelchair ◽  
Negative Obstacle

The objective of this work is to develop a negative obstacle detection algorithm for a robotic wheelchair. Negative obstacles — depressions in the surrounding terrain including descending stairwells, and curb drop-offs — present highly dangerous navigation scenarios because they exhibit wide characteristic variability, are perceptible only at close distances, and are difficult to detect at normal operating speeds. Negative obstacle detection on robotic wheelchairs could greatly increase the safety of the devices. The approach presented in this paper uses measurements from a single-scan laser range-finder and a microprocessor to detect negative obstacles. A real-time algorithm was developed that monitors time-varying changes in the measured distances and functions through the assumption that sharp increases in this monitored value represented a detected negative obstacle. It was found that LiDAR sensors with slight beam divergence and significant error produced impressive obstacle detection accuracy, detecting controlled examples of negative obstacles with 88% accuracy for 6 cm obstacles and above on a robotic development platform and 90% accuracy for 7.5 cm obstacles and above on a robotic wheelchair. The implementation of this algorithm could prevent life-changing injuries to robotic wheelchair users caused by negative obstacles.

scholarly journals Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6861
Author(s):  
Marius Dulău ◽  
Florin Oniga
Keyword(s):  
Computational Complexity ◽  
Obstacle Detection ◽  
Benchmark Dataset ◽  
Processing Stage ◽  
Detection Approach ◽  
Lidar Measurements ◽  
Ground Point ◽  
Clustering Approach ◽  
Point Detection

In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

Follow-Based Forward Obstacle Detection Using Vision Insensitive Feature for Road Cycling

2013 ◽  
Vol 718-720 ◽  
pp. 2427-2431
Author(s):  
Jing Yang ◽  
Ming Gou
Keyword(s):  
Object Detection ◽  
Image Sequences ◽  
Obstacle Detection ◽  
Road Surface ◽  
Image Subtraction ◽  
Robust Detection ◽  
The Past ◽  
Road Cycling ◽  
Detection Approach ◽  
Road Surfaces

Paper proposes a method for detecting general obstacles on a road by subtracting present and past in road cycling camera images. The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning based methods detect only specific obstacles. To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-road cycling camera image sequences, and then registries road surfaces between the frames. Finally, obstacles are detected by applying image subtraction to the redistricted road surface regions with a vision insensitive feature for robust detection. Experiments were conducted by using several image sequences captured by an actual in-road cycling camera to confirm the effectiveness of the proposed method. The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even with different situations.

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