Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the "Keep It Small and Simple" (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.

I'm writing this post from the guest room in my mom's house, which is peppered with old knick-knacks of mine--to summon the spirit of my childhood room, I suppose. While flipping through my photo albums, I was tickled to find a blurry picture of the poster for Phone Booth, clearly taken by me on a disposable camera outside of a movie theater. I was probably too young to be watching a gunman thriller--thanks, Mom--but I'm pretty sure my affection for it had a lot to do with Colin Farrell, who was a relative unknown when that movie came out in 2002. To this day, I'm a bit gaga over him, though I think part of the reason my puppy love has turned into something more enduring is that, as I've gotten older and my tastes have evolved, so has the actor's persona. Not to downplay his macho heartthrob phase in the aughts--I still go catatonic whenever I think about him salsa dancing in Miami Vice, and I sense noted MV-heads Bilge and David feel the same way--but it has been a delight to see him take on increasingly stranger, more cerebral roles for directors like Yorgos Lanthimos and Sofia Coppola while also pushing himself, unafraid to get ugly and unhinged, in blockbusters like The Batman.