to an arbitrary frame of reference (a few typos in [6] were fixed by [PERSON] [7]), that by [PERSON] [8] which finds the intersection points of n spheres in P\({}^{\text{n}}\) based on Gaussian elimination, and the line intersection algorithm proposed by [PERSON] et al. [9], as well as the geometric solution developed by [PERSON] and [PERSON] using Cayley-Menger determinants [2], etc. ; and numerical methods which provide optimal estimation by minimizing the residue of (1) in some form, such as least-squares estimation [8; 10; 11], Taylor-series [12], the extended Kalman filtering (EKF) [13; 14; 15], linear algebraic techniques [8; 16], and particle filtering [17; 18], etc. Our work in [19] proposed a closed-form algorithm for the non-linear least-squares trilateration formulation that is dominantly addressed with numerical methods, which provides near optimal estimation with low time complexity. Correspondingly, range sensors, which provide direct distance measurements based on the time of flight of the signals, have been dominantly used in trilateration systems, such as those based on ultrasonic [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34], radio frequency (RF) [18; 17; 19; 25; 26; 27; 28; 29; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45], laser [5], and infrared [46] signals, just to name a few. Existing ranging-based trilateration systems mostly adopt a GPS (Global Positioning System)-like configuration with either \"fixed transmitters + onboard receiver\" or \"onboard transmitter + fixed receivers\". They require installing and maintaining relevant signaling devices in the target environment. The accuracy of the time-of-flight measurement depends on the accuracy of synchronization among the transmitters and receivers. Trilateration for moving objects needs to accommodate the time difference among sequentially received ranging signals and the effect of movement. The signal delay caused by obstacles, which is not easy to detect and filter, is a common source of localization inaccuracy. An onboard ranging-based self-trilateration system, which transmits ranging signals and positions itself based on reflected signals, is highly challenging to realize, due to the difficulty in identifying references from only the reflected signals. Meanwhile, cameras have become major onboard sensors for autonomous mobile robots in various navigation tasks, due to the comprehensive amount of information contained in images. Correspondingly, vision-based trilateration is considered a natural extension of the classical ranging-based trilateration. Stereovision-based trilateration systems have been proposed [47; 48; 49]. Compared with ranging-based trilateration which depends on signaling between the separate transmitter and receiver, vision-based trilateration determines the ranges to landmarks from onboard-taken camera images, and thus achieves self-trilateration given the correspondence of landmarks. Vision-based trilateration provides a complementary scheme which naturally avoids some aforementioned common issues with ranging-based trilateration, such as synchronization among signaling devices and obstacle-caused signal delay. Because a camera can capture multiple landmarks at the same time, a camera-based trilateration system can thus obtain simultaneous distance measurements from multiple landmarks. One intrinsic challenge for stereovision is to match the landmark projections in the two simultaneous images, in particular when the numbers of observed landmarks are different in the two images, the two cameras view different subsets of landmarks (which are only partly overlapped), or landmarks are projected close to each other on the same image. Mismatching in landmark projections will result in invalid landmark reconstruction, and hence may cause serious errors in localization. This problem can be avoided if trilateration can be achieved using one camera.