HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications

This paper deals simultaneously with the trajectory estimation and map reconstruction by means of a stereo-calibrated vision system evolving in a large-scale unknown environment. This problem is widely known as Visual SLAM. Our proposal optimizes the execution time of the VSLAM framework while preserving its localization accuracy. The contributions of this paper are structured as follows. First, a novel VSLAM approach based on a "Weighted Mean" of multiple neighbor poses is detailed and is denoted as HOOFR SLAM. This approach provides a localization estimate after computing the camera poses (6-DOF rigid transformation) from the current image frame to previous neighbor frames. Taking advantage of the camera motion, we conjointly incorporate two types of stereo modes: "Static Stereo" mode (SS) through the fixed-baseline of left-right cameras setup along with the "Temporal Multi-view Stereo" mode (TMS). Moreover, instead of computing beforehand the disparity of SS mode for all key-points set, the disparity map in scale estimation step is limited to the inliers of the TMS mode so as to reduce the computational cost. This strategy is suitable to be parallelized on a multiprocessor architecture and exhibits a competitive performance with the other state-of-the-art strategies in many real datasets. Second, we report a hardware-software mapping of the proposed VSLAM approach. To this end, a heterogeneous CPU-GPU architecture-based vision system is considered. Third, a thorough and extensive experimental evaluation of our algorithm implemented on an automotive architecture (the NVIDIA Tegra TX1 system) is studied and analyzed. We report hence the localization and timing results through experiments on five well-known public stereo SLAM datasets: KITTI, Malaga, Oxford, MRT, and StLucia datasets.