高动态环境下的傅里叶梅林变换视觉SLAM算法

Fourier Merlin transform visual SLAM algorithm in highly dynamic environments

针对视觉即时定位与地图构建(SLAM)中的静态假设限制其在动态现实环境中应用的问题,提出了一种在高动态环境下基于傅里叶梅林变换的视觉SLAM算法。首先,采用傅里叶梅林算法配准以补偿相机运动,应用帧间差分算法获取运动掩膜。同时利用短时密集连接网络进行语义分割确定潜在运动物体掩膜。结合运动掩膜与物体掩膜,获得最终的物体运动区域,剔除该区域的特征点。最后,依据稳定的静态特征点跟踪优化,提升位姿精度。实验结果表明,本算法的绝对轨迹误差与相对位姿误差相比于ORB-SLAM2减少了95%以上,相比于DS-SLAM减少了30%以上,验证其在复杂动态场景下具有良好的定位精度与鲁棒性,有效降低运动模糊与光照变化对运动检测的影响,同时,克服了传统动态SLAM难以检测非先验运动对象的弊端。

To address the limitations of the static assumption in visual SLAM for dynamic real-world applications,a visual SLAM algorithm is proposed,which is based on Fourier-Mellin transform for high dynamic environments.It involves Fourier-Mellin transform for motion compensation,employs frame differencing for motion mask generation,utilizes the short-term dense connection network for semantic segmentation to identify potential moving objects,combines motion and object masks to obtain the final object motion region,and eliminates the corresponding feature points in that region.Finally,the pose accuracy is optimized based on stable static feature points.Experimental results demonstrate a reduction of over 95%in absolute trajectory error and relative pose error compared to ORB-SLAM2,and over 30%compared to DS-SLAM.These evaluate its excellent localization accuracy and robustness in complex dynamic scenes.The impact of motion blur and lighting changes on motion detection is effectively mitigated,and the limitations of traditional dynamic SLAM in detecting non-prior motion objects are overcome.