SLAMBooster: An Application-aware Controller for Approximation in SLAM
This work addresses the problem of enabling dense SLAM on low-power embedded devices for robotics and mobile applications, representing an incremental improvement over offline approximation methods.
The paper tackles the high computational cost of dense SLAM algorithms on resource-constrained devices by introducing SLAMBooster, an online controller that dynamically adjusts approximation knobs, resulting in a 72% reduction in computation time and 35% lower energy consumption while maintaining localization accuracy.
Simultaneous Localization and Mapping (SLAM) is the problem of constructing a map of a mobile agent's environment while localizing the agent within the map. Dense SLAM algorithms perform reconstruction and localization at pixel granularity. These algorithms require a lot of computational power, which has hindered their use on low-power resource-constrained devices. Approximate computing can be used to speed up SLAM implementations as long as the approximations do not prevent the agent from navigating correctly through the environment. Previous studies of approximation in SLAM have assumed that the entire trajectory of the agent is known before the agent starts, and they have focused on offline controllers that set approximation knobs at the start of the trajectory. In practice, the trajectory is not known ahead of time, and allowing knob settings to change dynamically opens up more opportunities for reducing computation time and energy. We describe SLAMBooster, an application-aware, online control system for dense SLAM that adaptively controls approximation knobs during the motion of the agent. SLAMBooster is based on a control technique called proportional-integral-derivative (PID) controller but our experiments showed this application-agnostic controller led to an unacceptable reduction in localization accuracy. To address this problem, SLAMBooster also exploits domain knowledge for controlling approximation by performing smooth surface detection and pose correction. We implemented SLAMBooster in the open-source SLAMBench framework and evaluated it on several trajectories. Our experiments show that on the average, SLAMBooster reduces the computation time by 72% and energy consumption by 35% on an embedded platform, while maintaining the accuracy of localization within reasonable bounds. These improvements make it feasible to deploy SLAM on a wider range of devices.